Policymakers and researchers increasingly seek to bring experts from diverse disciplines together to respond to complex societal challenges. Scholarship on interdisciplinary collaboration documents a growing body of evidence about what it takes for researchers from very different starting points to work together productively.

This raises a question relevant to those beyond the interdisciplinary research community: could lessons about bridging disciplinary divides also offer insights for bridging other kinds of divides — in particular, today’s conversation about affective polarization, the growing dislike and distrust partisans express toward the other side?

Most research on reducing polarization draws from social psychology, especially work on contact theory and identity. The team science and sociology-of-science literatures offer a complementary lens, framing disagreement partly as a problem of epistemic and semantic translation, rather than only one of group identity. This post explores where the two literatures overlap and what each might contribute to the other.

Image: Business Insider

What does research tell us makes collaboration work across deep differences?

Scholarship on interdisciplinary teams has repeatedly found that productive collaboration does not require participants to converge on shared worldviews. Historian of science Peter Galison (1997), in his study of twentieth-century physics Image and Logic, describes the “trading zones” in which physicists, engineers, and instrument-builders coordinated on particle accelerators despite fundamentally different theoretical commitments. Rather than resolving these disagreements, they developed pidgin vocabularies — local, pragmatic, partial — that allowed them to work together on shared instruments. A working shared language, anchored in concrete shared work, was what mattered.

This insight is extended in Star and Griesemer (1989)’s widely cited study in Social Studies of Science of Berkeley’s Museum of Vertebrate Zoology, which introduced the concept of “boundary objects”: representations, artifacts, or problems that are “both plastic enough to adapt to local needs and constraints of the several parties employing them, yet robust enough to maintain a common identity across sites.” Boundary objects — maps, specimens, shared questions — allow different communities to cooperate without fully agreeing on meaning. The shared problem, rather than a shared ideology, anchors collaboration.

The broader team science literature reinforces these findings. In a synthesis of research on transdisciplinary collaboration published in the American Journal of Preventive Medicine, Stokols et al (2008) identify sustained contact, institutional support, and psychological safety among the most consistent predictors of productive integration across disciplines. Psychological safety — Edmondson (1999)’s foundational construct, defined in Administrative Science Quarterly as “a shared belief held by members of a team that the team is safe for interpersonal risk taking” — is essential for teams tackling complex, ambiguous problems. In such environments, participants are more willing to acknowledge uncertainty, ask naïve questions across disciplines, and admit the limits of their own frameworks. Wuchty et al (2007), in an analysis of 19.9 million research papers and 2.1 million patents published in Science, further document that teams —often spanning multiple areas of expertise— now produce most high-impact knowledge across fields.

Together, these studies describe a particular model of productive disagreement: sustained time, a shared concrete problem, the gradual construction of new shared language, and a culture in which participants feel safe to expose the limits of their understanding.

What does the polarization literature tell us?

A striking feature of recent research on affective polarization is that many of its most promising interventions echo these same elements.

In a meta-analysis of 515 studies and 713 independent samples published in the Journal of Personality and Social Psychology, Pettigrew and Tropp (2006) found that intergroup contact typically reduces prejudice, with effects stronger under Allport’s optimal conditions — equal status, shared goals, cooperation, and institutional support. Field experiments have since extended this finding into polarized political contexts. Broockman and Kalla (2016), in a field experiment published in Science, found that a single approximately 10-minute non-judgmental conversation at voters’ doors substantially reduced transphobic attitudes, with effects persisting three months. In three field experiments involving 230 canvassers and 6,869 voters, published in the American Political Science Review, Kalla and Broockman (2020) found that the durable effect came specifically from conversations including the non-judgmental exchange of narratives, not from arguments alone. A further study, Kalla and Broockman (2021), in the American Journal of Political Science, isolates “perspective-getting” — listening closely to the story of someone in the outgroup — as the crucial mechanism, distinct from perspective-taking (imagining oneself in that position).

Other studies emphasize that polarization rests partly on inaccurate beliefs about what the other side thinks. Lees and Cikara (2020), across seven experiments with 4,282 participants in Nature Human Behaviour, found that partisans consistently overestimate the negativity of out-group views toward their in-group, and that correcting this misperception reduces hostile attributions. This maps onto a recurring pattern in interdisciplinary work: participants often assume more disagreement across disciplinary lines than actually exists, and that assumed distance itself becomes a barrier to collaboration.

What do these literatures share, and where do they diverge?

Can studies on interdisciplinary research inform work on polarization? Possibly. These areas of inquiry reflect important overlap: that structured cooperation on shared problems produces more traction than abstract debate over values. Both point to psychological safety and non-judgmental listening as preconditions for genuine exchange. And both suggest the goal of productive exchange across difference is rarely full agreement, but rather the construction of local, working understandings.

To apply lessons from interdisciplinary research, several limitations remain. Interdisciplinary collaboration is typically voluntary and rests on a shared meta-goal of understanding; many political encounters lack either condition. Research teams are small; polarization is population-scale. And while Levendusky (2018) , in the Journal of Politics, finds that priming a superordinate American identity (making people think of themselves first and foremost as Americans (a shared identity) rather than as members of opposing political groups (e.g., Democrat vs. Republican) can reduce affective polarization in controlled experiments, replication work points to the fragility of such effects, highlighting the distance between lab interventions and the structural forces driving political division.

This exploration suggests that lessons from team science are most applicable not to mass media environments, but to structured civic deliberation, cross-partisan coalitions working on concrete local problems, and bridge-building organizations that create something like “trading zones” for political difference.

Closing thoughts

Scholars of interdisciplinary collaboration may not have set out to address polarization, but their findings about bridging deep differences in vocabulary, method, and assumption speak to a broader set of challenges. Polarization, like disciplinary fragmentation, may be an artifact not only of identity but of the structures in which people are (or are not) brought into sustained contact around something that matters to all of them.

Further research explicitly connecting these literatures — tracing, for instance, how boundary objects might inform depolarizing civic projects, or how perspective-getting maps onto the pidgin languages Galison describes — could enrich both fields. For now, they converge on a diagnosis: the hard part of bridging difference is not usually changing minds, but building the scaffolding — time, language, shared concrete work, and psychological safety — within which minds can change.

Last updated: April 24, 2026

Do you have research, experiences, or insights to share on bridging divides — disciplinary, political, or otherwise? Reach out to me at rageorge@stanford.edu, and I would be grateful to feature this in a future post.

References

- Broockman, D., & Kalla, J. (2016). Durably reducing transphobia: A field experiment on door-to-door canvassing. Science, 352(6282), 220–224. https://www.science.org/doi/10.1126/science.aad9713

- Edmondson, A. (1999). Psychological safety and learning behavior in work teams. Administrative Science Quarterly, 44(2), 350–383. https://journals.sagepub.com/doi/10.2307/2666999

- Galison, P. (1997). Image and Logic: A Material Culture of Microphysics. Chicago: University of Chicago Press. https://press.uchicago.edu/ucp/books/book/chicago/I/bo3710110.html

- Kalla, J. L., & Broockman, D. E. (2020). Reducing exclusionary attitudes through interpersonal conversation: Evidence from three field experiments. American Political Science Review, 114(2), 410–425. https://www.cambridge.org/core/journals/american-political-science-review/article/abs/reducing-exclusionary-attitudes-through-interpersonal-conversation-evidence-from-three-field-experiments/4AA5B97806A4CAFBAB0651F5DAD8F223

- Kalla, J. L., & Broockman, D. E. (2023). Which narrative strategies durably reduce prejudice? Evidence from field and survey experiments supporting the efficacy of perspective-getting. American Journal of Political Science, 67(1), 185–204. https://onlinelibrary.wiley.com/doi/abs/10.1111/ajps.12657

- Lees, J., & Cikara, M. (2020). Inaccurate group meta-perceptions drive negative out-group attributions in competitive contexts. Nature Human Behaviour, 4, 279–286. https://www.nature.com/articles/s41562-019-0766-4

- Levendusky, M. S. (2018). Americans, not partisans: Can priming American national identity reduce affective polarization? The Journal of Politics, 80(1), 59–70. https://www.journals.uchicago.edu/doi/abs/10.1086/693987

- Pettigrew, T. F., & Tropp, L. R. (2006). A meta-analytic test of intergroup contact theory. Journal of Personality and Social Psychology, 90(5), 751–783. https://psycnet.apa.org/record/2006-07099-004

- Star, S. L., & Griesemer, J. R. (1989). Institutional ecology, ‘translations’ and boundary objects: Amateurs and professionals in Berkeley’s Museum of Vertebrate Zoology, 1907–39. Social Studies of Science, 19(3), 387–420. https://journals.sagepub.com/doi/10.1177/030631289019003001

- Stokols, D., Misra, S., Moser, R. P., Hall, K. L., & Taylor, B. K. (2008). The ecology of team science: Understanding contextual influences on transdisciplinary collaboration. American Journal of Preventive Medicine, 35(2S), S96–S115. https://www.ajpmonline.org/article/S0749-3797(08)00420-0/abstract

- Wuchty, S., Jones, B. F., & Uzzi, B. (2007). The increasing dominance of teams in production of knowledge. Science, 316(5827), 1036–1039. https://www.science.org/doi/10.1126/science.1136099

And yet, the evidence suggests that emergencies consistently produce exactly the opposite: fragmented, siloed, and discipline-bound responses. This post examines what the research tells us about why that happens and what, if anything, might change it.

What makes an emergency “interdisciplinary”?

The term complex emergency has a specific meaning in humanitarian and disaster studies, referring to crises involving multiple, interacting causes such as conflict, displacement, disease outbreak, food insecurity, infrastructure collapse, issues that unfold simultaneously and require coordinated responses across sectors. Kruczkiewicz et al (2021) discuss how “increasingly, we face compounding and interrelated environmental, socioeconomic, and political crises. Yet our approaches to these problems are often siloed, fragmented, and inadequate.” COVID-19 was their central case study — a pandemic that, during its peak, was colliding with violent conflicts, drought, displacement, and structural racial and gender inequality around the world.

The problem is not a lack of awareness that these crises are interconnected. The problem is that the institutions and knowledge systems we mobilize in response were not built for that interconnection.

Kruczkiewicz et al call for a number of pathways to for funding to help enable deeper interdisciplinary collaboration to bridge disaster-related sectoral siloes:

1) combined natural and social scientific core research on dynamics of compound disasters, especially in complex emergency settings,

2) urgent and/or short-term technical assistance for governments and other frontline actors, and

3) longer-term secondments for researchers to work in operational settings, as well as for decision makers to contribute to research agendas and projects that address pressing problems on the ground.

The pandemic was, among other things, a stress test for the disciplinary architecture of emergency response. Herrick et al (2022) describe how COVID-19 “straddles and challenges the boundaries between humanitarianism, development, and global health, three fields with distinct institutional cultures, funding streams, and conceptual vocabularies that have historically operated in parallel rather than together.” The pandemic did not respect those distinctions; the response did.

Meanwhile, Haldane et al (2021) in a 28-country study published in Nature Medicine, found that in country after country, “public health and health service delivery systems are siloed: their coordination is limited or ineffective, and they have separate referral processes and reporting systems.” This systemic fragmentation, they argue, was associated with weaker health systems resilience. Countries that managed COVID-19 more effectively tended to be those that had already developed mechanisms for cross-sectoral coordination — not those that scrambled to build them in real time.

At the governance level, a decade of 2015-2030 Sendai Framework for Disaster Risk Reduction’s implementation tells a similar story. The Sendai Framework was the first major agreement of the post-2015 development agenda of UN Member States on disaster risk reduction. Okunola (2025)’s recent analysis found that across five African countries, disaster risk governance remains characterized by “fragmented or overlapping frameworks that weaken coordination” and “parallel structures at the national level” that produce institutional inefficiency. Despite explicit calls in the Sendai Framework for integrated, multi-sectoral approaches to disaster risk reduction, institutional siloes have proven remarkably durable.

Source: Okunola 2025.

A field that studies crises — in disciplinary isolation

The disaster research field itself suffers from disciplinary siloes. Wolbers, Kuipers and Boin (2021) find in their systematic review of 20 years of crisis and disaster research that the field has remained remarkably static over two decades: single case studies dominate, methodological diversity is limited, and the focus has shifted from preparedness toward response. The authors call for the community to put more effort into drawing lessons across individual events, to uncover the patterns that connect them.

Their review suggests that the academic community tasked with understanding crisis is itself producing knowledge in silos, making it harder to identify patterns across events, develop generalizable theory, or inform more integrated policy responses. This knowledge fragmentation is embedded in the research and reflected in practice.

Figure: Methods in disaster research. Source: Wolbers 2021.

Figure: Thematic analysis in crisis management literature. Source: Wolbers 2021.

Why siloes persist: the structural story

A recurring temptation in this literature is to attribute the persistence of siloes to culture — to a kind of disciplinary tribalism that could be overcome with better goodwill, more workshops, or smarter convening. But the evidence increasingly points to structural drivers that are harder to address.

Pimentel et al (2023) argue that despite decades of calls for interdisciplinarity, “research communities continue to become more siloed and less impactful.” As discussed in another post, Berkes et al (2024) track over 150,000 biomedical researchers and find that the most interdisciplinary researchers are also the most likely to exit academia early. Highly interdisciplinary early-career researchers stopped publishing far sooner than their more conventional peers: roughly half of the most interdisciplinary researchers had stopped publishing within eight years, compared to more than twenty years for moderately interdisciplinary ones. And those who remained in academia tended to become less interdisciplinary over time, apparently in response to career pressures. Interdisciplinary researchers, in their phrase, “fall through disciplinary divides.”

Xiang, Romero, and Teplitskiy (2025), analyzing nearly 129,000 manuscripts across 62 STEM journals, found that papers high in topic interdisciplinarity — those bridging multiple subject areas — face lower acceptance rates in peer review, even when their underlying knowledge base is broad. The incentive structure, in other words, actively penalizes the kind of work that complex crises most urgently demand.

A structural mismatch at the heart of crisis response

Taken together, this body of research points to a troubling mismatch. Complex emergencies are, by definition, interdisciplinary problems: they require the integration of epidemiology, political science, logistics, economics, climate science, and local knowledge, among other domains. But the academic and institutional systems that generate and deploy knowledge in response to those emergencies are organized around disciplinary specialization, rewarded for disciplinary depth, and penalized — structurally, not just culturally — for crossing those lines.

Kruczkiewicz and colleagues call for redesigning “the institutional rules and administrative structures of response organizations and research institutions” — a formidable agenda. The Sendai Framework’s own mid-term review process has acknowledged that integrated, interdisciplinary approaches to complex risks remain an aspiration rather than a reality, with progress slow across most countries. Neither diagnosis nor prescription is new.

What is perhaps underappreciated is the degree to which the problem is self-reinforcing. If interdisciplinary researchers leave academia early, the next generation of emergency scholars is trained in narrower silos. If topic-bridging papers face peer review penalties, the literature on complex emergencies remains fragmented. If institutional frameworks for disaster governance continue to operate in parallel rather than together, the response to the next compounding crisis will look much like the last.

This post is part of a living literature review on interdisciplinarity, supported by Coefficient Giving (formerly, Open Philanthropy). Have a paper you think belongs here? Get in touch.

This review synthesizes findings from five studies examining how multiple disciplinary inputs and related cross-disciplinary analysis contribute to improving analytic outcomes for security gains. Evidence indicates that boundary-crossing approaches improve analytic transparency, conceptual coherence, and collaborative capacity, though causal links to downstream security outcomes remain under-evaluated. The review concludes by outlining a framework based on existing studies from which future agendas can build.

This post is part of Bridging Boundaries, a living literature review on interdisciplinarity.

Image credit: The League of European Research Universities (LERU)

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National security institutions increasingly confront problems whose causes, dynamics, and consequences span multiple domains of expertise. Enhancing cyber security requires expertise related to technical architecture, behavioral vulnerabilities, and organizational processes. Disinformation campaigns involve political psychology, network science, platform design, and geopolitical strategy. Climate-related instability intersects environmental science, economics, social context, and conflict analysis. Most contemporary security threats are not merely complicated but fundamentally interdisciplinary.

In nuclear security, Sagan (1993) highlighted the important reality that organizations with stronger internal technical expertise are better at identifying failure pathways and preventing accidents. In conflict prevention and response, ensuring right technical experts are brought together to avoid the greatest national security harms remains a security imperative.

Despite this reality, national security organizations have historically been organized along disciplinary and bureaucratic lines. Analysts specialize; agencies compartmentalize; research programs divide expertise into technical or social domains. As a result, collaboration across boundaries is often ad hoc rather than institutionalized. Understanding how interdisciplinary collaboration works, and what concrete benefits it produces, has therefore become an urgent issue.

The Case for Interdisciplinarity in Security

The National Academies of Sciences (2019) finds that social and behavioral science research has substantial potential to improve analytic tradecraft, yet its integration into intelligence institutions has historically been inconsistent and unsystematic. This gap reflects institutional and structural barriers rather than lack of relevant knowledge. The report therefore frames interdisciplinarity not as an aspirational ideal but as an operational requirement for effective intelligence analysis.

The report also highlights the need for interdisciplinary design of analytic tools. Technologies such as artificial intelligence, advanced visualization systems, and distributed analytic platforms depend on insights from psychology, cognition, and organizational science to ensure that human analysts can interpret outputs correctly and collaborate effectively. Interdisciplinarity thus operates at both analytic and technological levels.

The strongest empirical evidence in the literature related to interdisciplinarity and national security concerns intermediate outcomes rather than direct causal links to national security performance.

· First, interdisciplinary engagement can improve conceptual coherence. Cains et al (2022) demonstrates that structured cross-disciplinary dialogue can identify definitional inconsistencies and produce shared frameworks, enabling more consistent analysis and coordination.

· Second, structured analytic methods can improve transparency and evaluability. Artner et al (2017) show that analytic processes often lack documentation, making it difficult to assess reasoning quality. Interdisciplinary teams using structured methods are better positioned to produce auditable analyses and learn from past judgments.

· Third, interdisciplinary integration can enhance analytic ecosystems. National Academies of Sciences (2019) finds that combining insights from social science and technical disciplines can improve the design of analytic tools, forecasting systems, and human–machine collaboration architectures. These improvements can strengthen analytic performance at

Conceptual Barriers: Language, Definitions, and Epistemic Misalignment

Cains et al (2022)’s study of cyber-security researchers and practitioners working within a U.S. Army–funded collaborative research alliance uses semi-structured interviews with twenty-seven participants across academia and government to examine how experts defined key concepts such as “cyber security” and “cyber security risk.”

Drawing on thematic coding, network analysis, and comparative content analysis, the study showed significant variation in definitions across participants. Network density—a measure of conceptual connectedness—was substantially higher for “cyber security” (0.795) than for “cyber security risk” (0.317), indicating far less agreement about the meaning of risk than security itself. The authors also found that vocabulary used by developers often did not match terminology used by practitioners.

These findings provide some quantitative evidence of interdisciplinary divergence in national security. They also demonstrate that disagreement often occurs not at the level of conclusions but at the level of underlying concepts. In operational contexts, such misalignment can translate into inconsistent threat prioritization, incompatible analytic outputs, or flawed decision support.

The study suggests that early-stage conceptual alignment, through shared glossaries, structured elicitation, or conceptual mapping, is a necessary precursor to effective interdisciplinary intelligence analysis.

Structured Analytic Methods as Integrative Tools

With conceptual divergence a central challenge, a potential solution is structured analytic methods. Artner et al (2017) evaluate analytic methods designed to improve reasoning transparency, reduce cognitive bias, and enhance analytic rigor.

Despite widespread use of structured analytic methods across intelligence agencies, the community has rarely assessed whether they improve analytic quality. The research team conducted a pilot review of twenty-nine intelligence assessments posted internally over a two-week period, developing an evaluation framework capable of assessing analytic transparency, documentation of assumptions, and use of structured reasoning.

One key observation was that many analytic products referenced alternative scenarios or hypotheses without documenting how those alternatives were generated, under what assumptions or frameworks and drawing on which cognitive or disciplinary bases. Without such documentation, reviewers cannot determine whether analytic reasoning was systematic or intuitive, nor whether diverse domain expertise informed the analysis.

The study suggests how structured analytic techniques can serve two interdisciplinary functions: they enhance analytic rigor by externalizing reasoning processes, and they also create shared procedural frameworks that allow experts from different disciplines to integrate knowledge in transparent and auditable ways. In effect, structured methods can facilitate collaboration among specialists with distinct epistemologies.

Institutional Infrastructure for Boundary Crossing

Alongside well-designed analytic tools is a broader need to address structural barriers for interdisciplinary input into national security. Several studies emphasize that interdisciplinary collaboration depends on institutional environments intentionally designed to support boundary crossing.

Vogel and Tyler (2019) analyze five historical and contemporary U.S. Intelligence Community initiatives to identify what organizational designs make interdisciplinary, cross-sector collaboration effective for intelligence work. They find such collaborations are difficult to establish and sustain, typically depend on high-level champions, stable funding, and clear mission relevance, and often fail when leadership or budget priorities shift., and alignment between long-term research goals and short-term operational needs.

A policy report by Shapiro et al (2020) and colleagues analyzing defense research models highlights organizations that bridge academia, government, and industry. These collaborative structures must balance openness with security: they must maintain credibility with external researchers while protecting sensitive information and enabling cross-platform analysis.

The report identifies hybrid institutions—such as the Laboratory for Analytic Sciences (LAS)—as particularly effective. LAS enables sustained collaboration among academic researchers, industry experts, and intelligence personnel working on sensitive analytic problems. Government participants often serve multi-year rotations at the lab, allowing time for trust building and shared understanding. The lab also established a dedicated collaboration team tasked with facilitating communication, translating terminology, and coordinating research across participants.

Supporting evidence from research summaries on intelligence-community collaborations identifies recurring success factors, including leadership support, long-term commitment, communication mechanisms for assessing progress, and clear lines of communication with operational stakeholders. These factors underscore that interdisciplinarity is not merely a matter of assembling diverse expertise but of designing institutions that make collaboration durable.

Incentives, Career Structures, and Time Horizons

Incentives play a decisive role in determining whether interdisciplinary collaboration occurs and whether it persists long enough to produce meaningful results. Shapiro et al (2020) emphasizes that effective collaborative organizations address incentives across the entire research lifecycle: project design, resource allocation, data access, and publication. Without such alignment, participants may prioritize disciplinary outputs or organizational mandates over collaborative goals.

The National Academies of Sciences (2019) survey similarly notes that integration of social and behavioral science into intelligence analysis has often been inconsistent because institutional structures do not systematically reward such integration. Analysts and researchers operate under performance metrics that may favor speed, specialization, or short-term deliverables rather than synthesis.

Time horizon is closely related. Interdisciplinary teams typically require extended periods to learn each other’s assumptions, methods, and terminology. Short funding cycles or temporary assignments can prevent teams from reaching the stage at which integration yields analytic benefits. Long-term institutional commitment therefore emerges as a critical enabling condition.

Trust, Communication, and Cultural Translation

Trust is frequently discussed as a soft variable, but in national security contexts it has concrete operational implications. Collaboration often occurs under conditions of secrecy, classification, and bureaucratic compartmentalization. These constraints can inhibit information sharing and discourage open discussion.

Evidence from studies of intelligence-community collaborations indicates that successful interdisciplinary initiatives cultivate explicit norms encouraging participants to acknowledge uncertainty, ask clarifying questions, and share incomplete ideas. They also establish clear rules governing data access and communication so participants understand what can be shared and how.

Translation roles are particularly important. Individuals or teams responsible for bridging disciplinary and institutional cultures help maintain alignment and prevent misunderstandings. In the LAS model, a dedicated collaboration team performs this function, illustrating that translation is not incidental but an institutionalized responsibility.

AMulti-Level Framework for Interdisciplinary Input for Security

Synthesizing across the studies reviewed, interdisciplinary national security collaboration can be conceptualized as a layered system:

Individual level: communication skills, openness to uncertainty, familiarity with structured methods.
Team level: shared definitions, documented reasoning, agreed analytic procedures.
Organizational level: boundary-spanning institutions, aligned incentives, secure data environments.
System level: integration of human and machine analysis, evaluation mechanisms, long-term research agendas.

Alignment across these levels creates reinforcing effects that enhance analytic capability. Misalignment at any level can undermine collaboration even when other components function well.

Conclusion

The literature reviewed supports a conclusion that interdisciplinary collaboration is not merely beneficial but essential for contemporary national security analysis. Complex threats require analytic approaches capable of integrating diverse forms of expertise and data. Empirical studies demonstrate that interdisciplinary practices can improve conceptual clarity, analytic transparency, and collaborative effectiveness—core elements of sound security decision-making.

However, interdisciplinarity does not emerge automatically. It must be deliberately designed through institutional structures, analytic tools, incentive systems, and trust-building practices. National security organizations that invest in these enabling conditions are more likely to develop the analytic capacity required to understand and respond to complex threats.

Still significant research gaps remain. The field lacks systematic evaluations linking interdisciplinary methods to downstream security outcomes. Addressing this gap will require new measurement strategies, longitudinal studies, and institutional experimentation, efforts still central to the future effectiveness of national security institutions.

References

Artner, Stephen, Richard S. Girven, and James B. Bruce. 2017. Assessing the Value of Structured Analytic Techniques in the U.S. Intelligence Community. Santa Monica, CA: RAND Corporation.

Cains, M. G., et al. 2022. “Defining Cyber Security and Cyber Security Risk within a Multidisciplinary Context Using Expert Elicitation.” Journal of Cybersecurity.

National Academies of Sciences, Engineering, and Medicine. 2019. A Decadal Survey of the Social and Behavioral Sciences: A Research Agenda for Advancing Intelligence Analysis. Washington, DC: National Academies Press.

Shapiro, Jacob N., et al. 2020. Collaborative Models for Understanding Influence Operations: Lessons from Defense Research. Washington, DC: Carnegie Endowment for International Peace.

Vogel, Kathleen M., and Beverly B. Tyler. 2019. “Interdisciplinary, Cross-Sector Collaboration in the U.S. Intelligence Community: Lessons Learned from Past and Present Efforts.” Intelligence and National Security.

But research indicates that, despite shared challenges across research fields, some researchers are more likely to conduct interdisciplinary research than others. Within these studies is indication from a small but growing number of studies that find more senior women can be more likely to cross disciplines in their research than men. This is the subject of this post.

While there is some indication that women tend to conduct more interdisciplinary research than men, more research is needed into the motivations, norms, structures, and processes that underlie these gender differences.

More women, more disciplinary collaboration?

Large scale survey analysis from Rhoten and Pfirman (2007) and biliometric analysis from Science-Metrix (2019) find that women may exhibit a stronger preference for interdisciplinary research than men. Science-Metrix found among Canada’s NSERC awardees that female researchers exhibited higher rates of highly interdisciplinary papers (those in the top 10%) than male researchers (11% vs. 9.2%). Rhoten and Pfirman find women particularly express preference towards team collaboration and problem orientation.

Araújo et al (2017) similarly found in a study of more than 270,000 scientists in the natural sciences that women with many collaborators are more likely to have collaborators from other fields than their male peers. These effects differed, however, in other fields, showing that gender effects are not consistent across all areas.

What explains these differences remains subject for further research. Some suggest that women tend to be more collaborative in general, though men are more likely to collaborate with other men. Other studies suggest that norms or behaviors related to communication skills, social sensitivity, flexibility and social impact can drive women to be more collaborative in research and reaching across disciplines than men.

Pinhero et al (2022) posit that data may be impacted by a measurement bias due to the fact that women self-cite less than men (see Anderson et al 2019). Because interdisciplinarity is often measured by the diversity of cited references in publications, self-cites can can reduce the impression of interdisciplinarity. But when controlling for a potential self-citation bias, they still find that women are positively associated with interdisciplinarity in their study.

Source: Pinheiro et al 2022

But… Are Junior Women Researchers Doing Less Interdisciplinary Research?

And yet, further studies have suggested this affect can reduce or even reverse when disaggregating researchers by seniority in their fields. Liu et al (2023) found in a study of 675,135 doctoral theses from 1950 to 2016 in five scientific disciplines that male doctoral students were more likely to undertake interdisciplinary in their research than their female peers, perhaps reflecting greater flexibility and authority afforded to males than females. They found that these disparities have become more pronounced since 2000 and among top-tier (R1) universities, and that the presence of a female doctoral advisor can negatively impact the interdisciplinary nature of research.

Source: Liu et al (2023)

Smith-Doerr and Croissant (2016) similarly find in an ethnographic study of chemical scientists that junior female scientists are sometimes discouraged from pursuing interdisciplinary research that is perceived as “riskier.” Their interviews uncovered that despite expectations that women may be well suited to interdisciplinarity based on perceptions about women as being more collaborative and interested in societal impact, the “gendered cultural expectation that does not match up to the reality of the costs and benefits in practice.”

Closing the gap?

Given indication that junior women researchers tend may face greater hurdles to interdisciplinary research than male peers, more research is needed into what drives these differences and potential solutions.

“If women, relative to men, are more likely to perceive interdisciplinary work as too high risk, and if funders do not take action to correct the potential consequences of such perceptions, the overall research ecosystem could be negatively impacted.” - Pinheiero et al

Smith-Doerr and Croissant describe a “catch 22” for women researchers, who simultaneously face norms urging them to collaborate alongside incentives to maintain disciplinary silos. They write,

“Women scientists encounter both expectations to collaborate, and, at the same time (through discouraging advice from mentors and peers), significant disincentives to do interdisciplinary and collaborative work.” - Smith-Doerr and Croissant

The interactions of the trends examined in these studies with wider gendered dynamics in academic and research spaces requires deeper scrutiny. Indication that women sometimes serve as productive bridge builders in interdisciplinary collaborations requires deeper analysis too. Without reinforcing gendered stereotypes, the implications of known attrition of female scientists as seniority advances in many fields merits scrutiny, if this could be linked to potential reductions in interdisciplinary collaborations, given the societal emphasis on interdisciplinary of innovation and research for societal good.

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Check out more from the Bridging Boundaries, a Living Literature Review on Interdisciplinary Research here!

So, if researchers and funders agree that interdisciplinary research is important for joint goals, what happens if funds begin to wane?

On the one hand, the potential benefit multiplier that can come from strong interdisciplinary research collaborations might incentivize funders to support these collaborations when funds are limited. Funders increasingly prioritize interdisciplinary approaches in their work, and researchers variously report benefits from these collaborations for the quality, reach, and impact of research. A few studies suggest that these incentives may strengthen under resource scarcity.

On the other hand, research on the roadblocks to funding these collaborations and some tracking to model what happens when research competition increases suggests interdisciplinary research could become harder to fund when overall resources dip. This post reviews evidence on each side, finding that there is reason to raise concern that interdisciplinarity may dip with funding reductions, but that other factors may shape outcomes in environments of increased competition for funds.

Why Funding Dips May Reduce Interdisciplinary Research

Image: NanoTemper

Foundational work from Bromham, Dinnage and Hua (2016) corroborates qualitative studies that have found interdisciplinary research can be harder to fund in general than disciplinary forms. Their study finds a negative relationship between funding proposal success and the interdisciplinarity of the proposed research. This could suggest these collaborations may be harder to gain funding for, a dynamic that could exacerbate with increased competition from reduced funds. Using bibliometric data of a large sample set to test funding success, they find the more interdisciplinary the research, the less likely it is to be funded.

Bromham, Dinnage and Hua (2016), p. 685

Bronham attribute the relationship between disciplinary focus and funding success to biases towards disciplines implicit in funders’ decision-making and/or insufficient evaluation systems and review norms that under-recognize the potential of interdisciplinary research.

Do these dynamics worsen, or make interdisciplinary research less likely to win funds when resources reduce?

Schweiger et al (2024) examine scarcity in research funding directly, empirically testing and modeling outcomes to find that high competition for research funding and increase biases, and can disadvantage projects that are harder to evaluate like interdisciplinary research. They highlight that the increased difficulties in identifying and selecting reviewers for interdisciplinary research can aggravate structural biases that could make these collaborations hardest hit when competition amplifies funding allocation problems.

These findings are corroborated by Miermans (2024) who finds using survey and qualitative research that researchers perceive that intense competition can narrow research agendas towards “safer” disciplinary research, and can increase the perceived costs of higher-risk interdisciplinary research.

Making more with less?

Why the Merits of Interdisciplinary Research May Increase With Fewer Resources

A broader review of available research nuances these findings, providing critical additional context. Some studies that explore various features of these questions could indicate that funding cuts may not directly reduce interdisciplinary collaboration and that certain strategies may help incentivize these collaborations when resources become scarce. Brown et al (2015) offer contrasting research to Bronham et al’s findings on the low success rate of interdisciplinary research funding, finding instead that these collaborations can sometimes be more successful at gaining grant funding than single discipline research given increased funder and public calls for interdisciplinarity. And a study of Australian researchers and university leaders by Newman (2023) found that cultural influences can matter more than material or financial factors (including time and research funding) in promoting or reducing the likelihood of interdisciplinary research.

Another factor that may influence funder- and researcher decision-making in resource scarcity relates to the perceived potential impact of potential research approaches. Abramo et al (2017) and Millar (2013) find that some forms of interdisciplinary research, such as certain social science and bio science collaborations and some early career research practices can result in higher impact publications than single disciplinary endeavors. While studies including Spence et al (2024) underline that early career researchers often lack effective and sufficiently adaptable support systems, resources, and training to optimize their work outside of disciplinary structures inside universities, a desire to pursue socially impactful research sometimes drew early career researchers and funders towards interdisciplinary research.

If funders and researchers identify these potential gains from interdisciplinary collaboration to include potential for more impactful work, these incentives could conceivably convince funders to prioritize interdisciplinary research when resources wane.

Predicting the Cutting Room Floor

Studies indicate that tracking rates of interdisciplinary research when resources wane will be important.

There is evidence to suggest interdisciplinary research could become less attractive for scholars, and harder for funders to allocate funds towards, when competition increases. At the same time, the perceived public value of interdisciplinary research - visible in the practices of many top research funders today - may help funders and scholars prioritize these collaborations when funds diminish, if effectively communicated and nurtured through institutional prioritization and support.

But overall research suggests that other factors beyond funding, particularly cultural ones, may play a significant role in the likelihood of interdisciplinary research. This indicates the imperative of a broader understanding of the intersection of funding dynamics with the wider ecosystem of practices, attitudes and institutions that can foster or hinder future trajectories for interdisciplinary research.

Interdisciplinary research will be impacted by increases in competition for research funds, just as disciplinary approaches face impacts too. Further work to track these impacts, and to understand how interdisciplinary collaborations may be uniquely affected, will be critical to ensure the benefits of these collaborations for researchers and funders are sustained and expanded.

• “Member States should ensure that AI governance mechanisms are inclusive, transparent, multidisciplinary, multilateral (this includes the possibility of mitigation and redress of harm across borders) and multi-stakeholder” - UNESCO, Recommendation on the Ethics of AI, 2023

• “Governments should consider long-term public investment, and encourage private investment, in research and development and open science, including interdisciplinary efforts, to spur innovation in trustworthy AI that focus on challenging technical issues and on AI-related social, legal and ethical implications and policy issues.” - OECD, Council Recommendation on Artificial Intelligence, 2019

Just as scholars and practitioners advocate for multiple disciplinary inputs into AI – AI itself offers promise to help people surmount disciplinary barriers (this topic is the subject of another post).

While there is widespread recognition of the importance of interdisciplinary input into the development of AI technologies and in their regulation, evaluation, and deployment, work to document strategies that can help bridge siloes in AI efforts remain nascent.

If cross-sector collaborations are essential for AI development, what works to bridge them?

Cross-sector expertise, AI, and innovation

Research generally identifies cross-sector expertise, multiple disciplinary inputs, and the ability to collaborate across sectors as factors that contribute to innovation. Scholars have built on David Guest’s (1991) framework views “T-shaped” professionals as an ideal prototype to drive innovation – individuals that hold strong disciplinary expertise (vertical bar of a T) alongside an ability to collaborate with other disciplines (horizontal bar). As built on by Hansen and Bertel (2023)’s framework, “vertical knowledge” covers discipline-specific expertise and “horizontal knowledge” bridges other fields in access and understanding, both of which are necessary for innovation. The relationship between these knowledge types and AI are mutually beneficial, while AI can benefit from individuals and teams with these capabilities, as Dell’Acqua et al (2025) found, AI can also contribute to improving them in teams and individuals.

What improves cross-sectoral collaborations?

While research is fragmented and somewhat nascent in the face of emerging technologies, existing studies identify several strategies and supportive conditions that can improve cross-sector and cross-disciplinary collaboration.

A fundamental challenge in improving cross-sector expertise in input into AI in research is identifying and measuring collaboration meaningfully. Without a tools to develop a baseline and meaningful metrics, it can be difficult to actively increase meaningful collaboration and monitor progress. Different metrics for interdisciplinary research exist (some of the different ways disciplinary collaborations are measured are discussed in another post – is interdisciplinary research on the rise?).

One significant finding in efforts to track collaborative research has been that funding can play a meaningful role in breaking down silos, as was demonstrated by Luke et al (2015) in the case of the National Institutes of Health’s Clinical and Translational Science Awards (CTSA) and their positive impact on cross-sector science collaborations.

Bryson, Crosby and Stone (2015) who have mapped theoretical and empirical frameworks on cross-sectoral collaboration call this work “needed and challenging.” They identify the landscape of frameworks that shape theoretical understandings of what drives cross-sector collaboration (Figure 1), mapping key insights in relation to areas of focus (on leadership vs. on governance, for example) and in terms of wider focus on enabling conditions, such as face-to-face dialogue and trust building.

Figure 1. Bryson et al (2015) mapping of theoretical frameworks on cross-sector collaboration

Source: Bryson, Crosby and Stone (2015)

They find that both “drivers” and “conditions” are necessary for cross-sectoral collaborations, noting that “even when environmental conditions favor the formation of cross-sector collaborations, they are unlikely to get under way without the presence of more specific drivers or initial conditions” (p. 652). They call for different types of structural and processual “ambidexterity” in organizations to meaningfully build relations, networks, and power sharing for effective collaboration (building on the concepts as defined by O’Reilly and Tushman 2013), and call for more research into the types of skills, strategies, and organizational designs to enable this.

Available case studies, though limited, identify a number of institutional conditions and strategies from these frameworks applied in practice. For example, Buylova et al (2025) find that governance innovations can help bridge siloes, drawing on the case of the EU Cities Mission. They found silos embedded in structures in different levels of organizations, and strategies that help bridge them include policy integration, institutional arrangements and collaborative processes, leadership, and shared ideas and visions. Their study identifies that silos that rely on political leadership are some of the least addressed.

What tools exist aiming to improve multiple disciplinary collaboration on AI?

A study focused on interdisciplinary collaboration between AI researchers and medical experts from Cao et al (2025) offers one approach – a set of twelve tools that identify as impactful for interventions. These include an AI-enhanced research glossary with personalized analogies, an agile platform to facilitate co-design, and an integrated resource management system. They find that after three months these tools enhanced team engagement, communication, trust, and collaboration outcomes.

Their proposed toolkit (the MedAI-SciTS Framework) (figure 2) proposes three phases, including early planning and design (foundational), project development (activation), design (co-design) and development/evaluation (validation).

Figure 2:

The complexity of the proposed 12-toolkit system (as acknowledged by the researchers) makes it difficult to implement in less apt settings. They authors suggest using the toolkit to build smaller interventions as feasible.

Another promising intervention area for researchers identified by studies to potential increase cross disciplinary AI collaboration is through conferences. Patel et al (2024) identify contributions from interdisciplinary conferences in advancing explainable AI in healthcare. They identify promising practices for these conferences to best bridge silos by purposively designing multidisciplinary sessions dedicated to application-driven challenges, calling for sessions that “outline real-world cases where explainability is essential but current AI falls short,” and for the publication of detailed conference proceedings to ensure findings are accessible to wider audiences as a route to foster further collaboration.

Figure 3: A model for interdisciplinary conferences to advance explainable AI in healthcare

Source: Patel et al (2024)

A critical agenda

This post reviewed several available studies that inform a discussion on the question: how can individuals and institutions increase multi-disciplinary collaboration on AI? Scholars and practitioners variously call for these collaborations. But more research is needed into the practices that can best be implemented by organizations of different types to bring different disciplines together productively. Useful frameworks help ground theory on the issues, alongside emergent toolkits that institutions can adapt and deploy. Further research on these issues—through context-specific case studies and rigorous large-N analyses—could help researchers and practitioners more effectively integrate and leverage the benefits of collaborative practices.

This post is a part of Bridging Boundaries, a living literature review on interdisciplinary with support from Open Philanthropy. Learn more about the project here.

Image Source: ThoughtCo

Language and terminology can impede or promote research impact. “Jargon” – technical or specialist terminology that general audiences outside of a group do not understand – is generally recognized as a barrier to effective research communication. But jargon is still everywhere in academic research and notoriously difficult to combat.

Studies from a variety of disciplines demonstrate how overly technical language impedes public communication of science. But there is limited examination of how interdisciplinary teams, who often communicate through distinctive sets of terminologies, are specifically affected by and navigate the use of technical language.

This post, part of the living literature review on interdisciplinary research, discusses the state of research on these issues. Identifying emerging knowledge on jargon’s impacts on forms of transdisciplinary, interdisciplinary, and multidisciplinary research, it also reviews available analysis on strategies to address jargon use, including emergent AI tools.

Is Jargon on the rise?

Although studies are limited, there is some indication that jargon use is increasing. Barnett and Doubleday, 2020 and Plaven-Sigray et al, 2017 track jargon use rises with increasing scientific specialization. The theory is that with increased specialism, scholars increase their use of specialist vocabulary.

But specialization may not be the only factor driving jargon use. Some studies suggest that academics are increasing their use of jargon over time more generally, which may indicate a decline in scholarship’s overall accessibility.

Definitively tracking this is difficult, as there are not available cross-disciplinary, meta analysis to track change over time with clear metrics. Offering one view, Baram-Tsabari et al, 2020 observe an increase in the use of jargon in the interdisciplinary journal Public Understanding of Science, aimed at scholars and practitioners. Reviewing three decades of the journal, they reviewed articles published in 1999/2000 (47), 2009 (49), and 2019 (65) using the “de-jargonizer”, a free open source service (scienceandpublic.com) that classifies each word into high, medium or rare frequency. They find that the use of jargon has increased over time, reducing the accessibility of papers. They find that “rare words” they identify (see their Figure 3, copied below, for examples) are sometimes so specialist that they appear in only one article. They conclude that the journal has “moved away from everyday language,” suggesting negative implications for the accessibility of research.

(Source: Baram-Tsabari et al, 2020)

How does jargon shape interdisciplinary research?

If there is an increase in jargon use among scholars, what does this mean for interdisciplinary research? Scholarship seems clear that research communication requires a shared vocabulary. Strober, 2006 identifies that work to align on a shared vocabulary is a building block for multidisciplinary engagement, and how without it, engagement between scholars of different fields is limited. Lucy et al, 2022, and Choi and Pak, 2007 frame words as “gatekeepers” for effective research communication, identifying unique terminologies within different scientific domains that can optimize communication within the field, but impedes communication and collaboration with researchers in other domains.

Lucy et al (2022) suggest that jargon use may be reducing not just the interdisciplinary production but also the consumption of research in multiple disciplines. Their study of discipline-specific terms as well as terms that hold different meanings across fields determines that jargon use is “nearly always” negatively correlated with interdisciplinary impact. They observe jargon use to be highest in the natural and hard sciences, and lowest in the social sciences (Figure 2 below) They find that abstracts in the same field typically contain less jargon in venues for wider generalist audiences (e.g. Nature) than when they are in discipline-focused ones (e.g. Genetics). They find that, “though multidisciplinary venues intend to cater to more general audiences, some fields' writing norms may act as barriers rather than bridges, and thus impede the dispersion of scholarly ideas.”

Source: Lucy et al 2022

Taguchi and Boden (2025) go further to suggest that jargon can even impede innovation, finding in a study of an interdisciplinary children’s education program that when jargon is automatized and internalized, it can hold the participating scholars back from meta-reflection and creativity.

Jargon: What can be done?

Scholars and technologists have developed a variety of tools that may help researchers identify and reduce jargon use, which could potentially enhance interdisciplinary collaboration and research use across multiple disciplines, increasing interdisciplinary impact.

Shi et al. (2018) offer a methodology, using neural language modeling, to pinpoint terms whose meanings drastically differ across fields, helping scholars highlight vocabulary that could confuse interdisciplinary collaborators. Willoughby et al (2020) offer an R script, published for public access, that scholars can use to quantify the amount of scientific jargon in their writing.

Can AI help?

Artificial Intelligence (AI) provides hope for bridging scholarly divides if scholars harness tools to identify and reduce jargon use.

Natural language techniques are being used to identify and simplify scholarly jargon (Gardner and Davies, 2013; Tanaka-Ishii and Terada, 2011). Guo et al 2025 recommend personalizing this process, highlighting the risk of identifying false positives and offering suggestions for training models to accurately detect jargon. There is a push, for example, to deploy these AI tools to reduce jargon in the medical field, to improve patient communication, with some promising models in improved patient care.

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Jargon is not all bad news – when appropriately deployed, it can improve the efficiency of communication within specialized groups, can help convey complex ideas, signal expertise, and can improve intra-group community building.

But research seems to indicate its use – which may be increasing over time – is an impediment to interdisciplinary collaboration. With indication that interdisciplinary research is not necessarily rising as hoped (the subject of another post), investment in the identification and reduction of jargon use in research publications, and tools to help cross-disciplinary teams combat it, could bring benefits for scholarly collaborations.

This post is part of a living literature review on interdisciplinary research, Bridging Boundaries. Learn more about the project here.

With these organizations in the United States facing budgetary cuts from federal funding and some facing closure, just as some universities also face widespread research funding cuts, could this have knock-on effects for interdisciplinary research production and use? This is the topic of this post. It explores the roles boundary organizations play in advancing interdisciplinary research collaboration and research translation to inform multiple sectors. It analyzes how these organizations differ from universities and how changes to the quality and quantity of their funding may affect rates of interdisciplinary research (rates which, as documented in another post, face some stagnation, despite widespread recognition of related benefits).

What are think tanks and boundary organizations and how do they impact society?

While definitions differ, think tanks and research institutes – sometimes referred to as “boundary organizations” – are important for bridging policy/practice divides. Wesselink and Hoppe, 2020 discuss how these “boundary organizations” shape policy progress in detail, arguing that they not only broker evidence but also produce unique knowledge and research.

Planells-Artigot et al (2021) find that their research contributions, measured by numbers of research publications, has been expanding since 2005. While official counts on the numbers of think tanks worldwide are limited, a study from McGann (2011) found that, at the time, of at least 6,480 think tanks in the world, 57% of which were based in North America and Western Europe. Despite a Western concentration, the study identified the greatest surge in their establishment unfolding at the time in Asia, Africa and the Middle East.

In general, studies find that these boundary organizations serve unique roles in comparison to academic organizations, government institutions, and private companies. One distinguishing feature is the speed with which they produce policy-relevant research (as described in Planells-Artigot et al (2021)). Another is their distinct position as networked knowledge brokers and policy advisors with proximity to policy spaces (as analyzed in Fraussen and Halprin, 2016). Yet another benefit stems from their unique strategies used to influence target groups, often through a distinct focus on media engagement, government testimony, and direct engagement with policy makers (as described in Rich et al, 2011 and Medvetz, 2012)) . Critics of these organizations suggest their research contributions are limited and that they often serve elite, government or business interests (see discussion in Plehwe, 2015). Comparative studies find that their influence in different countries and political contexts varies, as does their prominence, prevalence, and relationship to policymakers (Abelson, 2018).

Are think tanks more interdisciplinary than universities?

While not all think tanks conduct interdisciplinary research, studies suggest think tanks across multiple national and cultural contexts offer distinct benefits when it comes to interdisciplinary, multidisciplinary, transdisciplinary, and other forms of boundary-breaking research.

Some think tanks and boundary organizations mimic disciplinary structures within policy spaces and university organizations. But many create unique structures that can blend disciplinary teams more easily than is often feasible in other institutions. By contrast, universities often suffer from incentive structures that limit disciplinary collaboration . As Buanes and Jentoft (2009) document in interviews with academics in Norway, academic researchers lament that interdisciplinary research often lacks credit in terms of promotion, disincentivizing cross disciplinary collaborations in traditional academic settings. Such findings have been validated in other settings, such as in the United States, where university tenure practices tend to incentivize disciplinary focus and interdisciplinary research is often perceived as “riskier” (as documented by Rhoten and Parker, 2004, among others).

By contrast, studies find that think tanks and other research institutions more often include greater incentives for inter- or cross-disciplinary collaboration based on their institutional design. Qing et al, 2023, in their review of prominent definitions of think tanks from global scholars (see Annex 1 for a table of these definitions), find that think tanks can be particularly well suited for developing interdisciplinary and transdisciplinary research teams, and solving interdisciplinary policy problems.

“As the problems faced by think tanks are comprehensive and interdisciplinary, think tanks need to have the research and organizational ability to deal with multi-field, comprehensive and complex problems.” – Qing et al, 2023

One particular dimension of think tank work is their ability to advance interdisciplinary research through their networks and socialization activities. In the field of educational policy, Lawn and Normand (2015) find that think tanks have helped socialize diverse groups of researchers and policy actors to build common language and frameworks to inform policy, helping break down sectoral siloes and enabling productive multi- and inter-disciplinary discourse.

It is not simply the blending of disciplinary boundaries - such as work to bring together social science, natural science, engineering, and other models, that can produce “interdisciplinary research.” The blending of policy analysis and scientific research is itself seen by some as its own type of interdisciplinarity. Based on interviews with European think tank leaders, Bajenova (2016) recorded testimonies from think tank scholars who view their primarily function as a “bridge” between academic research and policy, seeing this itself a type of interdisciplinarity.

Jiaofeng (2020), exploring models of think tank research in China, finds that “think tank research, in nature, is a kind of interdisciplinary research” whereas “academic research, in contrast, is a kind of thorough exploration of a certain specific discipline” (p. 8). Jiafeng’s model sees think tank work as fundamentally interdisciplinary, blending three primary “knowledge layers” - data, scientific knowledge, and practical experience.

How do think tanks and research organizations advance interdisciplinary research?

Because policy problems often require multiple technical inputs, policy-facing research teams in boundary organizations are often built with multiple types of disciplinary expertise built into policy-focused teams. One example, as mapped by Peek and Guikema, 2021, describes an ideal research team focused on informing disaster relief policy would regularly engage teams with multiple disciplinary inputs.

Figure 2: Multiple disciplinary contributions to disaster response

Source: Peek and Guikema, 2021

While think tanks may foster interdisciplinary more easily, the depth of these collaborations may differ from conventional academic processes. Dalton et al (2022) observe that think tanks, especially their advisory bodies, often model top-down approaches to interdisciplinary collaboration designed to bring in experts from multiple disciplines inside teams, as opposed to bottom-up approaches based on individual agents who develop interdisciplinary collaborations. Both models, Dalton et al suggest, have relative benefits for research quality and impact. Top-down models, more common in think tanks, can miss organic opportunities for cross-team connection based on individual projects. But they offer the benefit of structural focus on collaboration. Bottom-up models can produce more impactful, organic research collaborations uniquely developed to solve problems, but rely on the provenance of individual research entrepreneurs and their personal connections.

Few studies document how think tanks prioritize their work through interdisciplinary research processes, though Fraussen and Halpin, 2017 offer a contribution. In their survey of Australian think tank professionals asking what motivates their decision to focus on an issue, they find that research gaps, alignment with organizational priorities are top motivating factors.

Figure 1. How think tanks prioritize issue focus

Source: Fraussen and Halpin, 2017

Several scholars raise concern that think tanks are becoming increasingly narrow, which may be blunting their effectiveness as tackling broad interdisciplinary policy questions. McGann (2003) noted this over a decade ago in the foreign policy field, arguing that think tanks were becoming narrower and more partisan. He remarked at the time, that, “[W]hile this trend has contributed to more focused research, it has also served to discourage truly interdisciplinary responses to complex issues and has limited the creativity of the scholars working to address policy problems.”

There are limited studies documenting the degree of specialization of think tanks and trends in their narrowing over time, though anecdotes suggest it may be an ongoing trend. A 2025 Quincy Institute evaluation of U.S. foreign policy think tank funds (Freeman and Cleveland-Stout, 2005) offers some lens into this question, observing a contemporary “prevalence of special interest funding” as a barrier to objective and transparent research and policy advice in the sector.

Research gaps

At a recent academic conference, a researcher at a foreign policy think tank remarked to me that one of the most impactful features of her career has been their ability to bring multiple sectoral policymakers into the room around cross-sectoral tech policy issues. In some cases, she shared that she even introduced civil servants working inside the same bureaucracy who had not met inside their own agency. These bureaucrats benefitted from think tank workshops as unique opportunities to engage in cross-cutting policy discussions. These benefits are a major feature of think tank work, but often go undocumented with limited studies on the fruits of these actions.

Future study into trends in interdisciplinary work within think tanks and boundary organizations, and the contributions these organizations amke to interdisciplinary research and practice, would offer a stronger evidence base for researchers, funders, policymakers, and practitioners to advance interdisciplinary collaboration. Gaps in current research suggest a need for further research into:

· The degree of interdisciplinary and other cross-disciplinary collaborations among different boundary organizations, tracking if and how this may be changing over time.

· The motivations and incentives that drive interdisciplinary research inside boundary organizations, and how these are similar or different to those in university research settings.

· The impact of funding on interdisciplinary research in boundary organizations, and what impacts may be of recent research funding cuts on boundary organizations in the United States for interdisciplinary research and policy.

This post is part of a living literature review on interdisciplinarity. Do you have ideas, experience, or research you’d like to contribute to the updating of this post or to ideas and content for future posts? Please reach out to Rachel George rageorge@stanford.edu to contribute.

Annex 1: Prominent definitions of think tanks

Source: Qing et al, 2023

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But what exactly is interdisciplinary research? And how does it differ from other, related terms?

In practice, term “interdisciplinary” is poorly defined - as are its distinctions from related terms like “multidisciplinary” and “transdisciplinary.” Literature raises a variety of criticisms about the subjectivity and lack of utility of related definitional exercises (see some of these critiques below - my emphasis added):

• “The academic literature on interdisciplinarity and transdisciplinarity shows no agreement over definitions. It shows plurality, heterogeneity and overlapping terms, even contested and contrasting discourses. Diverse definitions of inter- and transdisciplinarity coexist within the literature and are reproduced by researchers and practitioners.” – Vienni-Baptista, 2023

• “…the highly subjective nature of these terms makes it difficult to see the difference between the categories, with the result that they are often used interchangeably…” – Newman, 2022

• “Research in the multidisciplinary–interdisciplinary–transdisciplinary environment is not a set of mutually exclusive categories. Research is too complex…to be put into boxes that ignore the particularities of context…” – Klein, 2008

• “The endless typologies, classifications, and hierarchies of multi-, inter-, and transdisciplinarities are not helpful” – Graff, 2015

Despite clear concern among scholars about the limited utility of these terms, studies continue to work to define and raise distinctions between forms of disciplinary collaboration. Current research includes work to explore when certain types of collaboration are optimal, and to map relative benefits and drawbacks of different boundary-spanning approaches.

This post summarizes prominent existing definitions of these terms and presents current knowledge on the comparative benefits of different approaches.

Image source: OpenLearn

Interdisciplinary, multidisciplinary and transdisciplinary research: What’s the difference?

One prominent source studies draw on to clarify distinctions between these categories is the Organisation of Economic Co-operation and Development (OECD), which sets out the following definitions for terms (as covered in Klein 2017):

• Multidisciplinarity (OECD) Refers to“juxtaposition of various disciplines…[and] fosters wider scope of knowledge, information, and methods. Yet, disciplines remain separate, retain their original identity, and are not questioned.”

• Interdisciplinarity (OECD) Refers to “simple communication of ideas to the mutual integration of organizing concepts, methodology, procedures, epistemology, terminology, data, and organization of research and education”

• Transdisciplinarity (OECD) Refers to “[a] common system of axioms that transcends the scope of disciplinary worldviews through an overarching synthesis”

These definitions center the distinctions between terminologies around three potential characteristics: “integration” (for interdisciplinarity), “juxtaposition” / “separation” (for multidisicplinarity), and “transcendental” (for transdisciplinarity). Klein, 2017 sets out these distinctions, with cognates and other sub-categories, in Figure 1, which highlights how these efforts variously bring together disciplines from applying multiple disciplines but keeping them distinct, to focusing on blending these, to transforming the interaction to create something new.

Figure 1

Source: Klein 2017, in Frodeman et al, p. 22

Klein also provides a range of categories and a typology to explore the degree to which interdisciplinarity is integrated into research approach, methods, and process (not covered in detail in this post but with concepts to be focused on in future posts).

Another prominent source mapping these distinctions stems from a systematic review (Aboelela et al, 2007) which explored a spectrum of approaches based on their degree of “synthesis,” (see Figure 2).

Figure 2

Source: Aboelela et al, 2007

This model considers low-synthesis approaches to disciplinary collaboration (including “instrumental” interdisciplinarity), where research can be informed by different perspectives, to moderate-synthesis approaches (including “epistemological” interdisciplinarity) where approaches are re-structured, to high-synthesis approaches that work on synthesizing and fusing approaches.

Some questions one might consider in light of these studies when designing collaborative research teams drawing on multiple disciplines:

• How separate are the teams and processes for input in research based on discipline?

• To what degree are theories, concepts, frameworks, methods, and epistemologies blended across disciplines or distinct?

• Is collaboration focused on collation of disciplinary ideas, bridge-building, or transformation?

Are certain forms of disciplinary collaboration more impactful?

Given these approaches and terminologies, is one version best? Which type of research is most impactful? And what are the advantages of different types of disciplinary research collaborations?

Studies remark confidence that crossing disciplines, sometimes connected to “boundary spanning,” can bear multiple benefits for research, including by advancing innovation and societal impact. But this type of disciplinary collaboration through research is also sometimes difficult to pursue, develop, and disseminate. There remains very limited research that fully evaluates the impacts of these different types of collaborations.

Some benefits to multi-disciplinary research over more integrated and synthesized models appears to be the maintenance of traditional forums and pathways for academic quality and recognition. Research from Zuo and Zhao, 2018 finds that more multidisciplinary institutions are not necessarily more collaborative, although they do feature collaborations that are more interdisciplinary.

Interdisciplinary research is often perceived as “riskier” as opportunities for publication and academic recognition are less established than research that sits clearly within a discipline, though sometimes early career researchers are more apt to take these risks as they are seeking collaboration and publication opportunities (Rhoten and Parker, 2004).

Leahey, 2017 finds that interdisciplinary research sometimes takes longer and has a “productivity penality” due to the “required time for learning of new concepts, literatures, and techniques, and communication difficulties within interdisciplinary teams”. Leahy particularly finds peer review to serve as a barrier for interdisciplinary methods, “because interdisciplinary offerings get caught in the middle and cannot be viewed and evaluated clearly”… which “scholars to navigate the peer review system and publish IDR [interdisciplinary research].”

Despite barriers and challenges, the payoff for interdisciplinary models can be big. One study finds that it is easier to gain grant funding for interdisciplinary research projects as a result of rising demand by funding agencies (Brown, Deletic, and Wong, 2015). Another study found that interdisciplinary researchers are more likely to find a job after completing their PhD than those based in a single discipline (Rhoten and Parker, 2004). Leahy, 2017 finds that, though interdisciplinary researchers are less prpoductive, they are more prominent (as indicated by citation counts).

The most integrated and transformational approaches of transdisciplinary research are lauded as potentially bringing the biggest payoffs – a “powerful tool for societal change” (Research on Research Institute, 2024). But these potentially require the most resource intensive supports. They usually include academic and nonacademic partnerships, values driven approaches, and societally impactful outcomes (Woods et al 2024), but usually demand even more intensive resources and time for deep collaborations and receive less support than lighter approaches to interdisciplinary work may require.

Optimal models : What does the literature recommend?

Avoid imprecision

Scholars lament the current imprecision in existing use of terminologies related to disciplinary boundary crossing. Choi et al in 2006 made the recommendation that appears to continue to ring true to avoid these categories unless they are precisely used. “Multiple disciplinary” as a broader term for disciplinary collaborations could offer a generalist concept that avoids incoherence from overlapping terms.

“We further propose that when the exact nature of a multiple disciplinary effort is not known, the specific terms “Multidisciplinary”, “interdisciplinary”, and “transdisciplinary” should be avoided, and the general term “multiple disciplinary” used instead.” – Choi et al 2006

Align approach to goals

Is the goal to maximize impact across sectors? Is it to promote certain forms of network building? Is it to support a researchers’ personal career growth and trajectory? Different levels of integration bring different benefits and drawbacks. Tracking and expanding the research on the relative benefits can help inform researchers in strategies for collaboration.

Consider best practices and look to expand necessary supports

A 2006 AAAS symposium for funding agencies (as discussed in Klein, 2008) considered some of the supportive requirements for evaluating and forming interdisciplinary research collaborations. Their recommendations include the use of “on-the-fly” electronic review teams, interpreters to bridge the epistemic gap among content experts, forming joint panels and “matrix” schemes that combine disciplinary reviews with interdisciplinary panels. They seek to “achieve equilibria between the familiarity and distance of non-expertise, between transparency and opacity, expertise and subjectivity, and between interdisciplinary appeal and disciplinary mastery.”

Supportive structures across research funding, evaluation, formation, and dissemination all appear necessary, and more time and resource intensive, and the requirements for such supports will depend on how deeply integrated and synthesized approaches aspire to be. Multidisciplinary approaches may suit best when resources and time are limited. More interactive approaches may bear stronger fruit when supportive environments are available across the research cycle to optimize impact.

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This post will be updated as a “living review” to capture future research.

Think something is missing? Interested in co-writing a future post or sharing your experiences and insights? Reach out to me rageorge@stanford.edu and would be eager to include your contributions.

Cuts in national government research funding in the United States this year, such as potential major cuts to the National Institutes of Health, may undermine advancements in interdisciplinary research, with knock-on effects for government. This is partly because, without government incentives for collaboration through funding opportunities, university and academic settings otherwise tend to drive more rigid boundaries between disciplines, undermining the potential for interdisciplinary innovation.

Policymakers are also an important consumer of interdisciplinary research, meaning government spending for interdisciplinary research may also drive government efficiency and effectiveness. Because policy choices and government programs are often based on information that blends science and technology with understandings of social and behavioral dynamics, interdisciplinary research can help inform government on how to improve public polices and improve service provision.

Is interdisciplinary research more useful for policy than other forms of research?

In general, studies find an association between interdisciplinary research and “research impact.” In a conventional sense, “impact” here refers to the influence of a piece of research to the wider research community, measured in numbers of citations in leading academic databases, although the term is also used to refer to the wider societal and policy impacts of a paper. Okamura, 2019 finds a positive connection between interdisciplinarity and research impact in terms of citation counts. Examining highly cited paper clusters, Okamura finds that an increase by one in the effective number of disciplines was associated with an approximately 20% increase in the research impact (Figures 1 & 2).

There is some evidence that, in broader interpretations of impact on government and society, interdisciplinary research attracts greater attention from policy audiences than research that comes from a single discipline. A study from Hu, Huang and Bu, 2024 during COVID-19 found a positive correlation between the interdisciplinarity of scientific publications and the attention they receive in policy documents related to public health. Their research found that more variety in the disciplinary inputs correlated with stronger policy uptake.

Another study from de Sandes-Guimaraes et al (2022) also identifies a positive correlation between interdisciplinary research and policy impact. They find that the combination of broad interdisciplinary research teams with co-production (or research methods that involve partnerships with intended end users in the design and delivery of research) provides the highest impacts on public policies. Examining the impacts of interdisciplinary research teams in Brazil’s Institutes of Advanced Studies, the research team found through interviews and document review that interdisciplinary teams with widest breadth in their interdisciplinary profiles had the strongest impacts on public policy, but this was facilitated through a secondary factor – coproduction of knowledge (or collaboration with intended users -policy makers – throughout the research cycle). Interdisciplinary groups with a wide breadth of disciplinary backgrounds that had an active participation of government partners in organizing events and producing joint scientific publications had the strongest impacts of their research on policy.

How do the benefits of interdisciplinary research look within specific policy areas? Levinson and Thornton (2003) highlight the work of an Environmental Protection Agency/U.S. Department of Agriculture “Water and Watersheds Research Program” as an example of policy-relevant disciplinary collaboration. A major goal of the program was promoting integration across biological, physical, and social sciences in the area of watershed management. The interdisciplinary makeup of teams helped the research by:

· Improving research questions and focus

· Freshening perspectives to the research

· Holding better appreciation of the complexity of the issues

· Gaining new tools and techniques for studying ecological systems

· Contributing to integrated conceptual models that better framed critical pathways and watershed processes

· Scientists and engineers from different disciplines taught each other new ways and approaches for investigating problems that each thought they had previously understood.

These benefits also helped improve policy relevance. They write, “emphasizing the integration of socioeconomic and political attributes, conditions, and consequences in these [scientific] projects brings interdisciplinary research into the policy and management realm.” They find especially that bringing social science perspectives into research on water and agriculture enhanced related policy relevance and impact.

Why does interdisciplinary research have greater policy impact?

One reason scholars posit for why interdisciplinary collaboration can have greater policy impact than traditional disciplinary study is because interdisciplinary research “compels careful listening and explicit attention to the distinctive requirements to writing and speaking to different audiences” These, Edward Balleston (2024) suggests, are “key skills to engage productively not only with other academics, but also broad publics and decision-makers.” It is these communication skills for “versatile communication” (see Figure 3), fostered alongside research, that can help facilitate research impact on policy.

Figure 3: Balleisen, 2024

How do policymakers engage in interdisciplinary collaborations?

Policy actors utilize insights from across disciplines in a variety of ways. They also engage in cross-sectoral collaboration (sometimes, “interagency” collaboration) in ways that replicate productive interdisciplinary research process. These collaborations are variously associated with policy success.

As a few examples of interagency experiences that build on the benefits of interdisciplinary research teams, but face ongoing challenges to implementation (there are many others):

• In a study of interagency collaborations between public health and environmental agencies in Wisconsin, Daley (2009) finds that performance evaluations, collaborative experience, trust, and problem agreement all variously shape the degree to which interagency collaborations report success. Mclaughlan (2022) finds that “interdisciplinary, interagency collaborations” are best practice for advancing policy responses to human trafficking. He finds a variety of barriers to optimizing this collaboration in practice, including minimal funding, barriers to hiring disciplinary diversity, and limited performance review-related incentives for interdisciplinary and cross-departmental ways of working that can better drive collaboration, where much engagement remains implemented through informal initiatives of individual public servants.

• Firmin et al (2025) explore emerging good practices for improving interdisciplinary interagency collaborations at the policy level, through a study of cross departmental collaboration for family service provisions. Their recommendations include co-locating professionals from different organizations and developing multi-agency protocols and information-sharing agreements to improve the variety of inputs that inform policies.

How can policymakers improve incentives for interdisciplinary research and its application?

Policymakers continue to call for expanded interdisciplinary research.

“Learning and practicing collaboration between and among disciplines are critical skills that investigators and teams need to develop.”
—Alicia Knoedler, NSF Office of Integrative Activities

“We need interdisciplinary teams that can apply AI to key problems and grapple with the societal impacts of AI.”
—Deirdre Mulligan, Principal Deputy U.S. Chief Technology Officer

“Interdisciplinary approaches encompassing all disciplines […] are essential to inspire renewed solutions to complex global challenges.”
—G7 Science and Technology Ministers’ Communique

There remains growing in interest in improving funding for interdisciplinary research (see the related post on funding). This can benefit government in a variety of ways. For example, Harris, 2010 finds that government funding for interdisciplinary research can help university research teams make breakthroughs in research, which can be variously helpful for government policy and practice.

Further attention to these relationships can help inform policymakers to better understand the relationships between interdisciplinary research and policy.

“Funders’ support is critical to achieving the potential value-added of interdisciplinarity, and these agencies have key roles to play, especially in shaping large-scale interdisciplinary initiatives.” - (Lyall et al, 2013).

Policymakers and practitioners tasked with addressing environmental crises, global health challenges, wars and conflicts, and other major challenges commonly draw on insights from across engineering, data science, social sciences, and humanities to inform solutions, even when such diversity in perspectives is not formally designed within existing institutional structures. As Hu et al, 2024 stress, “Interdisciplinary research is increasingly recognized as one of the solutions to today’s challenging scientific and societal issues.”

But, despite widespread buy in for interdisciplinary work in rhetoric, there remain many roadblocks to implementing interdisciplinary responses to such challenges in practice. Ensuring adequate, and sustainable, funding for such collaborations is one ongoing concern. As covered in a previous post, “Is Interdisciplinary Research on the Rise?” growing calls to bridge disciplines have not necessarily translated to commensurate increases in interdisciplinary research practice, and sectoral and disciplinary siloes continue to define most institutions.

Top research funders, including the National Academies in the United States, the National Research Councils in the United Kingdom, and the European Commission, increasingly call for interdisciplinarity collaborations in their funding decisions. While scholars continue to face obstacles to delivering these collaborations in practice, funding remains a critical lever to bridging the boundaries between disciplines that are otherwise held up by contemporary disciplinary divides.

What role(s) can funders play in promoting interdisciplinarity? This post explores the relationship between funders and interdisciplinary approaches, presents relevant trends and highlights promising practices.

How is interdisciplinarity funded?

Funders play a central role in unlocking the potential for interdisciplinary research. Academics often turn to research funding bodies to support collaborations that universities may not otherwise have capacity to facilitate. The extent to which funds meet the ongoing stated desire and need among researchers to expand interdisciplinarity remains difficult to measure. For one, debates about what constitutes interdisciplinary research (and how this overlaps with related conceptions of multidisciplinarity, transdisciplinarity, and so on) make it difficult to measure the quantity of funds that truly is reaching interdisciplinary collaborations (a complaint raised by Rylance, 2015, among others).

Despite these limitations, some metrics have emerged that help shed light on the quantum of funds that reach interdisciplinary work. Bromham et al, 2016 found, using a study of 18,476 proposals to Australia’s Research Council Discovery Programme over five years, that interdisciplinary research proposals are less likely to be funded than narrower disciplinary projects (see Figure 1, in which the researchers mapped the effect of interdisciplinarity on successfully funded proposals). This study corroborated an ongoing perception across the academy that interdisciplinary research is positive for society, but impractical for researchers who require strong disciplinary engagement to attract research funding to maintain and sustain their work.

Figure 1

While it may be true that interdisciplinary research is less likely to become funded, one study suggests this weakens when looking to a longer time horizon. This suggests interdisciplinary research can be risky for junior researchers, but can have longer-term payoffs for attracting funding once careers are established. Sun et al, 2021 found, using a study of 44,419 research grants awarded by research councils in the UK, that interdisciplinary researchers tend to have a lower initial impact in their research (as measured by accrued citations, a point that corroborates the work of Levitt and Thelwall, 2008 who found that citations from single-discipline papers were about double that of papers that incorporate multiple disciplines), but in the long run, have stronger performance in attracting funding than their counterparts (see data in Figure below).

They conclude that this long-term benefit could serve as a north-star to encourage early career researchers to sustain activities despite initial hurdles, writing that, “pursuing an interdisciplinary career may require perseverance to overcome extra challenges, but can pave the way for a more successful endeavor.” Overall, the study suggests that persistence matters when it comes to interdisciplinary research, given that interdisciplinary (“cross-council”) approaches can be highly regarded based on funding amounts in the long-term.

What roles do funders play in advancing interdisciplinarity?

“Interdisciplinary research is increasingly regarded as the key to tackle contemporary complex societal challenges and to stimulate scientific innovation^. As high-impact discoveries often occur at the intersection of disciplines, scientists have become more engaged in research areas that transcend the boundaries between traditional fields, and increasingly collaborate across such boundaries.” – (Sun et al, 2021)

Funding may be a critical tool to unlock interdisciplinarity’s potential, though the exact role funding plays in enabling interdisciplinarity’s transformative possibilities remains under-explored. A study by Kwan, 2021 used bibliometric data to find that interdisciplinary research is positively associated with uniqueness in its contributions to technology development when it has funding support, but that this association disappears when funding is not present. This effect may be mediated by the fact that interdisciplinary research can be difficult to fund as it runs against established sectors and siloes that can build connections and feed into existing university structures, and thus, those that do gain funding may be higher quality. Still, this study, though also limited in its approach to only studies in one year time frame, offers an innovative model for exploring the specific role funders may play.

If interdisciplinarity is so critical to advancing solutions to societal challenges, how can funders help move the needle against forces that challenge interdisciplinarity in practice, including the practical limitations and incentives impeding collaboration in universities? Lyall and Fletcher, 2013 call for greater research to uncover the exact roles funders can play in moving the needle despite ongoing institutional constraints where disciplines dominate. They write, “[m]any countries’ research funding organisations now support funding schemes that require interdisciplinary collaboration but there is uneven guidance on the effective conduct of such research.”

They highlight that these funds have helped unlock innovations in research to respond to societal challenges, especially given the fact that many academic institutions still present boundaries to disciplinary collaboration. Between 2008 and 2011, they document wide scale buy in for interdisciplinary programs in the United Kingdom based on major cross-council funded research initiatives led by a single council in partnership with councils from other disciplinary homes, manifesting in large, multi-year program investments that formalize partnerships across several sectoral and disciplinary areas (see examples in Figure 2).

Figure 3: UK Research Council Investments in Interdisciplinary Research Programs

While they identify a variety of fruitful ongoing interdisciplinary funded programs, they find these efforts engage researchers with “academic conventions, structures and norms which are still predominantly discipline based.” Thus, they contribute to an ongoing call within research raising the importance of continued monitoring, advocacy, and programming that leverages funders’ power and influence to support researchers who continue to face these constraints.

Park et al (2023) identify a paradoxical finding: their large N study of 50,000 grants from 1985 to 2009 finds that, disciplinary grants, particularly are more effective in producing high-impact interdisciplinary research than those that are conventionally framed as “interdisciplinary.” Their study points to the potential weakness in explicitly interdisciplinary projects in reaching deep disciplinary audiences before bridging them, and potentially in the structural barriers in the academy that can weaken an interdisciplinary project when it is funded outside of disciplines.

While funding may be a critical tool, it is not the only factor that determines interdisciplinarity’s success. Another study raises the limitations of funding: Baum and Bartkowski, 2020 stress in a study of interdisciplinary collaborations in sustainability research in Europe that funding is “important but insufficient on its own,” arguing that other core factors beyond conventional funding models critical for thriving interdisciplinarity include:

- Support for longer-term interdisciplinary ways of working as a core mission of an institution, rather than only through temporary funding programs.

- Support for cultural change, including (and especially) for efforts that bridge misunderstandings and divisions between social science and natural science that often lead to limited engagement across this divide.

While it is true that these recommendations are not necessarily related to funding in a conventional programmatic sense, and are “beyond funding” so to speak according to the researchers, they could guide funders to contribute to addressing these causes financially. For example, funders could draw on these recommendations to fund core costs for the construction of permanent interdisciplinary institutions, or they could offer financial support for researchers to come together through meetings, networks, and initiatives that support exchanges across disciplines and advance mutual understanding, including between the academy and policymakers that benefit from these collaborations.

What are good practices for funders to promote interdisciplinarity for policy impact?

Few roadmaps exist to guide interdisciplinary funding to maximize its impacts on bridging fields for impact, especially given the heterogeneity of initiatives that fall under the umbrella of “interdisciplinarity” and related terms. Still, scholarship offers some insight into what works to improve funders’ efforts in this regard. A prominent study offering guidance for funders from Lyall et al, 2013 is based on the analysis of a set of case studies of UK environmental research collaborations and offers a number of promising practices. This study reviewed the experiences and outcomes in several funded collaborations, to ask how funders can best supporting policy-relevant interdisciplinary research.

Overall, Lyall et al find that funders’ internal and external practices can variously amplify or degrade the positive benefits of interdisciplinary research. Asserting that “[f]unders’ support is critical to achieving the potential value-added of interdisciplinarity…,” they identify the following recommendations for funders based on testimonials from interdisciplinary teams. They find, finders should:

· Model good interdisciplinary collaborative practice among themselves (including good communication, shared ownership and the gradual development of a collective vision across program teams within a funders’ own organization).

· Consider the importance of phased and ongoing forms of support that help prime and sustain interdisciplinarity collaboration, including early-stage program support that helps align expectations and build teams, ongoing research-policy translation support, and follow-on grants to encourage continued collaborations.

· Build flexibility into budgets to allow for continued learning about what works to support interdisciplinary learning, given there is no “one size fits all” model documented for interdisciplinary research process.

· Support thoughtful review and evaluation, especially given the fact that interdisciplinary efforts usually lack the established metrics enjoyed within established disciplines.

· Support researcher capacity building, including training for interdisciplinary PhD researchers.

· Engage knowledge brokers to help translate interdisciplinary research to diverse policy audiences and to help forge relationships where disciplinary barriers have prevented cross-sectoral learning.

Among examples raised in their research of tangible ways funders have helped prevent common pitfalls interdisciplinary teams face, Lyall et al highlight the U.S. National Science Foundation’s Dynamics of Coupled Natural and Human (CNH) Systems program, that has a specific program manager from each of the three funding directorates working as a team, with rotating leadership annually to help ensure that each directorate has equal ownership. Overall, the study stresses the important role(s) funders play in bridging disciplinary boundaries in university settings where researchers are otherwise facing boundaries that otherwise impede productive collaboration.

Closing thoughts

Interdisciplinary research and practice are critical to ensuring productive solutions to societal challenges. A deeper exploration of the links between interdisciplinary research and policy impact to respond to connected societal challenges on the agenda of top funding private and pubic funding bodies remains a topic for a future post. But one critical factor within this agenda is how funders can help address ongoing barriers to collaboration that researchers face in academic institutions. Funders play a clear role in advancing interdisciplinarity, especially as “outsiders” to academia that can help bridge disciplinary divides that are otherwise deeply entrenched. They may have limits, as Baum and Bartkowski stress, they can only go so far. Greater evidence on the specific benefits and limitations of funding as a means for advancing interdisciplinarity remains an area ripe for research.

And, while scholarship documents a number of emerging and promising practices that can serve as guideposts for funders, further exploration of these experiences across funders, recipients, and settings will help broaden our understanding of exactly how funders can amplify the supposed benefits of financial supports for driving impactful research outside of disciplinary boundaries. This will be all too vital given the growing consensus that such collaborations are necessary for adequate responses to today’s most pertinent societal, technological, and environmental challenges.

Last updated: April 6, 2026

Do you have research, experiences, or insights to share on how to advance interdisciplinary work, through funding or otherwise? Reach out to me at rageorge@stanford.edu, and would be grateful to feature this in a future post.

“Human researchers can barely scratch the surface of the potential of interdisciplinary transfer due to its cognitive difficulty and the very large number of combinations of similar societal problems. If AI transfer learning could be applied to IDR, it could open up vast possibilities for learning about societal problems…” – Baum, 2020

The artificial intelligence (AI) revolution brings both promise and perils across many domains, and its potential to influence the boundaries between disciplines is no different. As discussed in a previous post, is interdisciplinarity on the rise?, there are loud calls among researchers and practitioners to bridge the boundaries between sectors and disciplines through multidisciplinarity, transdisciplinarity, and particularly, “interdisciplinarity,” with significant societal and institutional buy-in. Among areas of hope raised by interdisciplinarity’s proponents, many anticipate that large language models, innovations in data processing tools, and other AI-facilitated technologies will help reduce the barriers that maintain disciplinary and sectoral siloes, unlocking potential for societal progress and innovation.

Is AI the solution to interdisciplinarity’s problems? Can technology, especially tools that help facilitate data knowledge accumulation, sharing, and access, serve as the key to bridging the boundaries that have otherwise proven so persistent and sticky in modern research and practice?

AI is in many ways fertile testing ground for efforts to bridge disciplines. Scholars increasingly call for multiple disciplinary perspectives to inform AI tool development. They often call for inputs across the hard sciences, social sciences, and humanities to shape AI tool development, AI safety and related issues. In particular, scholars and practitioners increasingly have brought together engineers with those in social science and ethics to monitor and inform AI tools for bias and discrimination.

The Center for AI Safety highlights this call for inputs from multiple disciplines, writing on its website, “We pursue conceptual research that examines AI safety from a multidisciplinary perspective, incorporating insights from safety engineering, complex systems, international relations, philosophy, and other fields…”

AI may also offer promise itself for facilitating cross-disciplinary research and practice through various processes– not only by serving as a site to apply multiple disciplines, but also by itself providing tools for researchers to use to break down disciplinary barriers. By helping bring together large volumes of data and information and expanding access to that information and speed with which it may be process, large language models and other tools offer potential to help researchers access and process more information, more quickly, which may facilitate greater opportunities to engage across disciplines.

The AI revolution also comes with risks that could harden disciplinary distinctions and expand siloes. AI’s basis in large data and quantitative models and the methods used to process information could build up the access to data and related methodologies at the expense of human-led qualitative reasoning, leading to fears that AI could challenge threaten the demise of qualitative research, or at least would harden the distinctions between particular fields where AI tools will have different effects.

Will AI help bridge these disciplinary boundaries, either by applying AI tools to research, by applying interdisciplinary research to AI questions, or both? Can AI facilitate what scholars and practitioners call for by way of expanded networked knowledge communities, in place of ongoing sectoral siloes?

This post explores the state of knowledge on the intersection of AI and interdisciplinarity, highlighting the status of insights around promising practices and potential pitfalls.

The importance of interdisciplinarity for AI

Interdisciplinary collaborations relating to AI might improve research quality in a variety of ways. Some posit that disciplinary collaborations between AI specialists and other disciplines can help foster innovation and help solve societal challenges.

A study by Abbonato et al (2024) explored the question of whether interdisciplinary collaborations that involve AI and medicine are particularly impactful, noting the rise of studies starting in 2021 in which scholars “quickly embraced the idea of adopting AI techniques to tackle the challenges presented by COVID-19” (the authors cite DeGrave, Janizek, & Lee, 2021, Khan, Mehran et al, 2021; and Roberts, Driggs et al, 2021 as part of this hype). Abbonato et al’s study reviewed the impact of around 15,000 interdisciplinary papers bringing together AI researchers and medical researchers covering issues related to COVID-19, noting a rise in collaborations between AI researchers and medical researchers with the rise of the pandemic in 2021 (Figure 1). Their paper seeks to explore whether interdisciplinary research teams were particularly impactful. They model the question using two primary measures of research impact– the number of citations received by an interdisciplinary publication and the Altmetric attention scores of these publications – and one measure of “interdisciplinary spread” based on the team’s coding of the disciplinary composition of research teams.

Figure 1

Their findings muted optimism that AI was the key to research impact. They found that these interdisciplinary collaborations between medical professionals and AI specialists largely resulted in publications with “low visibility” and “low impact.” They posit that interdisciplinary research teams that engage AI researchers with other disciplines face a variety of challenges when applying AI techniques to other disciplines, challenges that include:

· Poor data quality

· Lack of global standards and database interoperability (the ability to translate formatted data across multiple platforms)

· The inability of algorithms to work without sufficient knowledge domain

· Overly exacting computational, architectural, and infrastructural requirements

· Legal and ethical opacity associated with privacy and intellectual property

These challenges may be mounted if advancements are made, for example, to synergize data standards improve data quality, in turn improving the potential to apply AI tools to a variety of disciplines. Beyond this, the authors caution against viewing disciplinary homes of authors as a measure of the potential of AI collaboration impact, and rather, suggest that the quality and depth of interdisciplinary knowledge alongside diverse research methods and framings may ultimately drive research impact. They write, “[w]hat appears to ensure the impact of a publication is, above all else, is the interdisciplinarity of the knowledge mobilized via its references, that is the actual epistemological diversity of the research conducted by a team” (p. 10). Their study suggests, in sum, while interdisciplinary research involving AI holds promise for advancing impactful research, blunt efforts to bring AI researchers together with other disciplines will not alone advance research impact and could even have adverse effects.

If one contends that interdisciplinarity is critical for AI-related research, what are some relevant best-practices for doing so?

Another study exploring the intersections of interdisciplinarity, AI, and research quality from Bisconti et al (2022) probed how to maximize interdisciplinarity’s benefits for AI research. Considering the importance of various disciplinary inputs to the development of the European Union’s Artificial Intelligence Act the authors, a diverse disciplinary group of eleven researchers, begin by asserting interdisciplinarity’s importance for AI policy. They write, that work on AI “requires an interdisciplinary approach to balance the technical issues and forecast biases, paying attention to the weight that these measures have on policy decisions” (p. 1444). Assuming the imperative of ensuring interdisciplinary inputs for AI, they probe potential good practices for applying interdisciplinary research inputs to AI governance, noting various challenges to applying interdisciplinarity in practice. These include divergences they noted in their research between different conceptions of AI among researchers spanning different fields. They call for researchers to build consensus around an agreed “methodology to formalize an interdisciplinary process aimed at increasing the synergies of ICWR research groups dealing with AI systems with urgent social implications” (p. 1451).

“Without a process theory that enables effective group communication and understanding, interdisciplinarity is not sufficient to ensure that the solutions and implementations of AI systems are addressed in the best manner.” – Bisconti et al, 2022

Their proposed methodology (Figure 2 below) focuses on bridge-building through common narrative and semantic exercises that build a common rhetoric and framework for research. Their method for interdisciplinarity in AI research based on this process is as follows:

· Stage 1: Facilitate the communication of different understandings on the same issue, and work in interdisciplinary teams to define hypotheses.

· Stage 2: Set up a common lexicon.

· Stage 3: Narrow down the possible applications and implications of the AI system in a real-world scenario.

· Stage 4: Conduct follow up assessments, with developed metrics on how significantly “interdisciplinary” goals were researched, to inform further iteration.

Figure 2

Source

The importance of AI for interdisciplinarity

Just as AI-related research may benefit from interdisciplinarity, interdisciplinarity may, too, benefit from AI tools. A paper from Baum (2020) explored the question: in what ways can artificial intelligence assist with interdisciplinary research for addressing complex societal problems and advancing the social good? Baum contends that interdisciplinarity is critical for work on many modern societal challenges, writing, “[p]roblems such as environmental protection, public health, and emerging technology governance do not fit neatly within traditional academic disciplines and therefore require an interdisciplinary approach” (p.545). Baum then notes a number of challenges to the application of interdisciplinarity in practice, including: Institutional divides that discourage disciplinary engagement and collaboration; Large literatures making access to vast amounts of external knowledge unrealistic; Peer review systems that are a barrier to bridging specialism; Challenges in transferring knowledge from one domain to another.

Introducing the concept of “Artificial interdisciplinarity (A-ID)” – “Artificial intelligence that performs interdisciplinary research or supports other agents in the performance of interdisciplinary research,” he probes the areas where AI may help advance interdisciplinarity already given the current state of technology. He finds the following potential areas where AI may facilitate improved interdisciplinarity:

· Search engines: AI can help challenge disciplinary divides by providing tools to explore vast literatures in other disciplines.

· Recommendation engines: AI can provide recommendations customized for individual user profiles, as currently exists through Google Scholar and Elsevier.

· Automated content analysis: Applying machine learning to analyze literature content.

He then identifies “Immediate-term” A-ID systems, those that go beyond current capabilities in the near future. These include:

· Interpretation: Using AI tools to interpret research publications through LLMs.

· Translation: Helping researchers translate knowledge across epistemic divides, including between languages (e.g. English to Spanish) as well as conceptual divides.

· Transfer: AI can help transfer knowledge gained in one domain to another with similar features.

Finally, Baum suggests that “Long-term A-ID” with distant future progress through artificial general intelligence (AGI) may significantly improve the potential for interdisciplinarity, though projections are inconclusive, and a variety of ethical dilemmas also emerge.

Ongoing Challenges

While there appears to be significant consensus around the importance of interdisciplinarity for AI research, studies variously explore the practical challenges for doing so. Kusters et al, 2020 in their paper raise the question: How can an interdisciplinary approach towards AI benefit from and contribute to the AI revolution? They call for viewing the relationship between interdisciplinarity and AI as a “two way street,” recognizing that AI should be applied to a variety of fields, and that AI should be informed by a variety of fields. Citing a number of examples, most prominently Frontier Development Lab, they insist that efforts to bring multiple disciplines together productively through AI research efforts is “not utopic,” and already achievable. They call for ramping up similar efforts.

Among challenges, the authors call out reforming educational systems to better meet the needs of this “two-way street” will be critical. They write, “Of course, combining AI with other fields is not without challenges. Like any time when fields synergize, barriers in communication arise, due to differences in terminologies, methods, cultures, and interests. How to bridge such gaps remains an open question, but having a solid education in both machine learning and the field of interest is clearly imperative.”

A question remains in how to scale these efforts that aim to bring AI and interdisciplinarity together. To what degree might all disciplines require some grounding in topics related to machine learning? And, if they must, how might this be institutionalized within educational systems at scale?

Final thoughts

Scholars continue to debate the merits of disciplinary boundaries, but overall, calls remain loud to bridge them. Is AI the solution? AI appears to offer potential towards a “two-way street” model. In terms of applying interdisciplinarity to AI research, the field has proven fertile testing ground for applying multiple disciplines to inform and shape its development and regulation. AI may also itself help facilitate the bridging of other disciplines by offering new tools for processing and accessing large amounts of information.

AI will likely facilitate revolutions in the ways that scholars can access and process information. As AI improves the speed and scale with which scholars can interact with a diverse set of inputs and information, the once restrictive barriers of time and resources for processing information that once made working across disciplines too onerous could reduce and weaken, presenting new opportunities. Yet, as scholarship reviewed in this entry has noted, new challenges emerge, with AI applications not yet enabling coherent and synergistic processes that fully bridge disciplinary boundaries, and the potential requirement for education systems to advance alongside AI development. With the rapid pace of technological advancement in AI, further study of the multiple ways that AI intersects with interdisciplinarity will require further documentation and scrutiny.

Articles Cited

Abbonato, Diletta, Stefano Bianchini, Floriana Gargiulo, and Tommaso Venturini. “Interdisciplinary Research in Artificial Intelligence: Lessons from COVID-19.” Quantitative Science Studies. https://doi.org/10.1162/qss_a_00329.

Baum, Seth D. “Artificial Interdisciplinarity: Artificial Intelligence for Research on Complex Societal Problems.” Philosophy & Technology 34 (2021): 45–63. https://doi.org/10.1007/s13347-020-00416-5.

Bisconti, Piercosma, and Davide Orsitto et al. “Maximizing Team Synergy in AI-Related Interdisciplinary Groups: An Interdisciplinary-by-Design Iterative Methodology.” AI & Society 38 (2023): 1443–1452.

DeGrave, A.J., Janizek, J.D. & Lee, SI. AI for radiographic COVID-19 detection selects shortcuts over signal. Nat Mach Intell 3, 610–619 (2021). https://doi.org/10.1038/s42256-021-00338-7

Khan, Muzammil, Muhammad Taqi Mehran et al. “Applications of Artificial Intelligence in COVID-19 Pandemic: A Comprehensive Review.” Expert Systems with Applications 185 (December 15, 2021): 115695.

Kusters, Remy, and Dusan Misevic et al. “Interdisciplinary Research in Artificial Intelligence: Challenges and Opportunities.” Frontiers in Big Data 3 (2020). https://doi.org/10.3389/fdata.2020.577974.

Roberts, M., Driggs, D., Thorpe, M. et al. “Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans.” Nat Mach Intell 3, 199–217 (2021). https://doi.org/10.1038/s42256-021-00307-0.

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Disciplines fuel the modern world. Whether structuring universities, shaping industry, or informing policy, disciplinary divides have helped organize most of humanity’s institutions throughout history.

But the consensus today is that these boundaries are often a barrier to progress and innovation. This has driven a longstanding movement to break and bridge these divides through interdisciplinarity.

Writing in 2000, Weingart and Stehr identified interdisciplinarity’s rising appeal, finding the term to generally “denote reform, innovation and progress” (p.xii). Fast forward several decades, and Repko, Szostak and Phillips (2020) find that the academy largely views interdisciplinary to be superior to the alternatives in most research settings. They remark, “[t]oday, disciplinarity dominance is being challenged by interdisciplinarity” (p. 27). They echo Porter and Rafols (2009)’s pronouncement that “[i]nterdisciplinary research seems almost universally acclaimed as “the way to go’” (p.2).

Growing calls for interdisciplinarity have been backed, in some places, by billions of dollars. The National Science Foundation, a large U.S. government research funding body for the non-medical sciences that disbursed $9.9 billion in 2023, writes on its website that it “gives high priority to research that is interdisciplinary — transcending the scope of a single discipline or program.”

But have these efforts worked? Has interdisciplinarity risen commensurately in response to these calls? How is interdisciplinarity measured, and is it indeed on the rise?

Is interdisciplinarity increasing?

Without agreed definitions and metrics to guide study design and replication, there are limitations in our abilities to measure and track interdisciplinarity in practice. Still, a growing number of studies help illustrate how interdisciplinary research has expanded over time and some of the limitations at play.

While definitions differ, interdisciplinarity primarily aims to bring approaches from multiple fields of study together, often through collaborative research modalities, and usually with a desired outcome to produce insights relevant to multiple fields. Aboelela (2006) in their systematic review of definitions and practices of interdisciplinarity find three primary characteristics of a common understanding of the concept, based on: the qualitative mode of research (and its theoretical underpinnings), existence of a continuum of synthesis among disciplines, and the desired outcome of the interdisciplinary research.

Stirling, 2007 suggests that efforts to measure interdisciplinarity require at least two forms of measurement surrounding the quality and quantity of disciplinarity inputs: “A satisfactory index needs information on the variety of disciplines, their balance (or relative frequency) and their disparity (the ‘distance’ between them).”

Two common metrics frequently deployed in studies that aim to track these characteristics include:

·      Co-authorship: Co-authorship is on its own an indicator for collaboration and innovation. A more refined metric for interdisciplinarity measures the disciplinary homes of co-authors both qualitatively and quantitatively. Moody, 2004 uses co-authorship as a metric, for example, to study interdisciplinary research rates in sociology.

·      Citation characteristics: Another common metric of interdisciplinarity tracks the disciplinary homes of citations inside a given publication. Researchers use a variety of approaches tracking how often academics cite research that is distinct from their disciplinary home, how close or far these disciplines are, and how many disciplines, in total are cited. Raasch et al, 2013 use measures that quantify the numbers of citations outside the author(s) home disciplines as a metric alongside co-authorship in their study on interdisciplinary research rates in open-source research.

What is the best evidence on rates of interdisciplinarity? A significant study from Porter and Rafols (2009) found that interdisciplinary research in the sciences was grew over thirty years by about 50%. Their study specifically explored six scientific research domains from 1975-2005. Using the metric of co-authorship, a surface investigation found a whopping 75% increase over the period in the degree of interdisciplinarity among co-authors for research papers reviewed. However, when digging deeper into the data, the increase in research collaboration was overwhelmingly occurring among “neighboring fields” that is, for example, a common collaboration in research bringing together sub-fields of science, but much less often bringing fields that are more distant together (such as hard sciences and social sciences, or sciences and humanities). Their measure of instances of interdisciplinarity that brought together researchers outside of neighboring fields was low, only about 5%. This was true even in newer fields, such as biotech, which initially emerged as a type of “interdisciplinary” field that brought together engineering and biological sciences. Their mapping of trends in biotech (figures 1 and 2 below) finds the field broadly nested in the macro-field of biomedical sciences, although new interactions emerge – for example, in 2005, there is an uptick intersection between biotech and materials science. Overall, they found that “science is indeed becoming more interdisciplinary, but in small steps.” The authors suggest calls for interdisciplinarity have had limits in practice, mainly achieving gains in connecting neighboring fields, with only modest gains in connecting fields that are otherwise far apart.

Figure 1.                                                    

Figure 2

 Raasch, Lee, Spaeth and Herstatt (2013) offer another view into the question, with somewhat similar results. They look specifically at the field of “open source” research, or research of publicly available information, and find some sticky limitations holding back a fuller expansion of interdisciplinary research in practice.  They also find some areas where interdisciplinarity is on the decline.  Using bibliometric and secondary data of 306 core open-source publications and over 10,000 associated reference documents over the period of 1999-2009, they report some decreases in interdisciplinarity in open-source research over the period reviewed, and highlight, a “tendency to return to disciplinary research.”

Even though their study focused on the sub-set of publications that are “open source” research, they believe their findings could be more widely generalizable. They point to similar findings in other fields – such as in entrepreneurship research (Landstrom and Lohrke, 2010) and strategic management (Nerur et al., 2008), that have documented a degree of increased fragmentation and separation into more homogenous disciplinary communities in a number of research communities

Figure 3

What do these trends look like from a global comparative perspective? Adams, Loach and Szomszor (2016) interrogate indicators of “interdisciplinarity,” and present indices of “multidisciplinarity” (research bringing two or more disciplines together) and “interdisciplinarity” (research that has cross-disciplinary outcomes) across funding/grants and publication outcomes. Comparing rates in the UK, Australia, Canada, the Netherlands, and Sweden, they find differential rates of interdisciplinary collaboration, with Germany sitting on the lower end and Sweden and the Netherlands on the higher end (Figure 4).

Figure 4

Finally, a study of Chinese academic papers spanning 106 disciplines from 1992 to 2022 from Rong et al, 2024 found an increase in interdisciplinarity in Chinese research. They identify that that 85% of disciplines saw what they call “critical years” where qualitative increases in interdisciplinary research (measured in cross disciplinary citations outside of the home discipline of the author(s)) occurred.

Are students learning more disciplines?

Trends in interdisciplinary research inform, but do not determine, related trends in interdisciplinary study. This data is variously measured in student exposure to interdisciplinary courses (measured by course enrollment) and by looking at data on the numbers of students who major in explicitly interdisciplinary majors. Again, definitions and metrics differ. Some examples of “interdisciplinary” majors include the study of cognitive science (combining, psychology, neuroscience, and linguistics), gender studies (combining history, social sciences, and literature), and environmental studies (combining physical and social sciences).

Through the 1970s, universities implemented a range of “new” majors and course tracks that encouraged interdisciplinary study among student bodies. In the United States, Deloitte’s “Data Studies” documented an increase in interdisciplinary studies graduates (of interdisciplinary majors) in the workforce by 2.68% from 953,786 in 2021 to 979,354 in 2022. This was while the number of overall college graduates slightly reduced from 2021 to 2022 in the face of the COVID-19 pandemic (National Center for Education Statistics). Deloitte’s study found that the largest single share of these graduates they labelled as “interdisciplinary” holding majors went to work as schoolteachers, though career trajectories of this group were unsurprisingly diverse.

A 2019 study by College Factual reported in 2019 that interdisciplinary studies was the 13^th most popular major nationally in the U.S., and saw a 4.3% increase from the previous year.

Implementing interdisciplinarity teaching through courses and majors in practice has its limits. One study of the implementation, and demise, of an experimental interdisciplinary writing course at the University of South Dakota (Hoodman and Huckfelt, 2014) found that one challenge in interdisciplinary teaching in practice is when instructors are “trying to do too much” all at once. They recommend in evaluating their course that the “mandated skills and requirements should have been fewer.” The complexity of interdisciplinary teaching can mean that both faculty and students can struggle to get on the same page, structure learning, and gain necessary skills when multiple disciplines are brought to instruction all at once. There is a rich literature on good practices for interdisciplinary teaching (a subject for another post).

What explains these trends?

“Many researchers know interdisciplinarity when they see it, but not all see the same thing, and that makes life difficult for research funders and policy makers”. – Adams et al, 2016

It is perhaps surprising that interdisciplinary research that goes beyond connecting neighboring fields are not increasing more.  This suggests relatively powerful barriers may be at play, given that the motivators are strong: research suggests that interdisciplinary researchers attain better long-term funding (Sun, Livian, Ma and Latora, 2021) and have greater impact (Okamura, 2019). Students exposed to interdisciplinary learning may also go on to make more money, especially for those in the sciences (Han, LaViolette, Borkenhagen, and Bearman, 2023). A detailed discussion about these studies and the question of the motivations and impacts of interdisciplinarity will come in a future post.

Part of the challenge may be one of effective measurement. For research, bibliographic analysis alone is often relied on as a metric, but Adams et al, 2016 have suggested this has missed other forms of collaborative inquiry. Are peer reviewers engaged in multiple disciplines? Have researchers engaged with multiple sectors in policy/research to disseminate their findings? A number of other factors could inform the ways that research could cut across disciplines across the research lifecycle beyond authorship and citation metrics.

Another challenge may lie in institutional arrangements. Many prominent U.S. institutions promoted interdisciplinarity through structural reforms – notably the University of California’s Commission on General Education and Harvard University’s reforms in the 1970s that offered interdisciplinary courses and curricula spanning multiple fields. Klein (1996) found that by the turn of the 21^st century the number of interdisciplinary programs had declined since the 1970s. But Klein argued but this missed another trend, whereby interdisciplinary activities were occupying larger amounts of staff time even when organizational charts did not formally place people in interdisciplinary functions. This had both advantages and disadvantages. This reflects a point made by Clayton (1985) documented in Chettiparamb (2007) that “overt interdisciplinarity” may not make the same gains as “the concealed reality of interdisciplinarity” that sometimes flourishes beneath “subject facades.” These insights form an argument that for a true understanding of interdisciplinarity and its benefits, research must track not only formal organizational structures and outcomes, but also informal practices such as network building, consultations and exchanges, and other areas where interdisciplinarity can take root and benefit research.

There are various structural barriers impeding interdisciplinarity in practice. These barriers pose critical questions about the potential for expansion in interdisciplinarity and will be discussed with focused attention in posts to come. Scholars point to the absence of established benchmarks in emerging interdisciplinary fields, biased assessments, and rigidity stemming from the structures and incentives driving discipline-based journal publishing as roadblocks to the implementation of interdisciplinarity in practice (Jacobs & Frickel, 2009; Li & Chen, 2020; Pfirman & Martin, 2010).

Final thoughts

This post posed the question: is interdisciplinarity growing? Relatedly, it investigates, is the ongoing attention to and acceptance of the practice in contemporary academic and industry discourse making a difference?

Interdisciplinarity is not on an inevitable rising trajectory, even though there is widespread recognition of its benefits. Across some metrics, specifically those measuring the degree to which scientific studies are broadening out to incorporate adjacent fields, there have been measured increases documented. Interdisciplinary studies appear to be enjoying modest but steady increases in present-day. But accounting across a variety of measures, interdisciplinarity faces ongoing hurdles to meet many institutions’ stated ambitions, especially for bringing distant fields together. Some metrics even mark a decline and retrenchment towards disciplinary fragmentation.

That said, metrics and studies are relatively limited. There remain opportunities for researchers and practitioners to more deeply interrogate trends in interdisciplinary research, the factors that drive it, and obstacles facing its delivery in practice.

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