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Conceptualizing Convergence
As an introduction to his talk, Dan Stokols (University of California, Irvine) highlighted four consensus reports produced by the National Academies of Sciences, Engineering, and Medicine that demonstrate interest throughout the field in understanding and promoting convergence, transdisciplinary team science, and interdisciplinary research. He noted that convergence and its definitions have continued to evolve. Its hallmarks include deep integration across disciplines as well as between academic disciplines and perspectives outside academia, responsiveness to specific and compelling problems as research drivers, and the coproduction of knowledge by researchers and diverse stakeholder groups, many of which are situated outside academia. The problems that drive interest in convergence could be considered “wicked problems,” a term coined by Rittell and Webber1 to refer to interlocking problems with multiple causes for which there are no clearly established entities responsible for finding solutions. Some 21st-century global challenges that meet this definition and have been driving interest in convergence include the environment, public health, technology, and sociocultural challenges. Each of these examples could also be considered a “super wicked problem,” a term coined by Levin and colleagues2 in 2012 to describe wicked problems that require time-urgent solutions (for instance, climate change and COVID-19).
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1 Rittel, H.W., and M.M. Webber. (1973). Dilemmas in a general theory of planning. Policy Sciences 4(2):155–169.
2 Levin, K., B. Cashore, S. Bernstein, et al. (2012). Overcoming the tragedy of super wicked problems: Constraining our future selves to ameliorate global climate change. Policy Sciences 45:123–152. doi:10.1007/s11077-012-9151-0.
A comparison between two National Academies reports, the 2014 consensus report on convergence3 and the 2019 workshop proceedings,4 reveals that conceptions of convergence are changing. During those 5 years, the scope of cross-disciplinary and transepistemic work broadened to encompass not only STEM fields but also the arts, humanities, and interest groups beyond academia. Researchers began to emphasize integration of the concerns and tools of transdisciplinary collaboration and convergence. They also began to explicitly consider strategies to create a culture of convergence, as well as an interplay between divergent and convergent thinking in research organizations, agencies, and college campuses. Finally, Stokols noted emerging questions about the impact of macrosocietal forces (anti-science views, the COVID-19 pandemic) on convergence, questions such as how convergence can continue as COVID-19 restrictions hinder normal opportunities for collaboration.
Stokols presented a convergence ecosystem model that situates key facets of convergence within concentric zones. At the center are individual team members, who are situated in an immediate sociospatial physical environment, such as a lab. These levels are embedded within a multiteam system such as a university campus. Each level is then affected by macro-scale influences such as national research policies and stakeholder groups. Convergence can be supported at each ecosystem level. At the levels of the individual and a team’s immediate environment, it is critical to cultivate collaborative and integrative competencies. Team-specific supports may include technological and environmental resources. System-level support may include institutional incentives for cross-disciplinary partnerships such as seed grants, credit sharing, and criteria for promotion and tenure that acknowledge the importance of collaborative research. At the macro level, societal events, national research policies, and funding from agencies and foundations can positively or negatively impact opportunities for convergence. Stokols noted that social and cultural issues such as public opinion about the value of science can also affect convergence opportunities but have received less attention in the literature. (The individual, team, and institutional levels are major foci of the 2015 report Enhancing the Effectiveness of Team Science.5)
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3 National Research Council. (2014). Convergence: Facilitating Transdisciplinary Integration of Life Sciences, Physical Sciences, Engineering, and Beyond. Washington, DC: The National Academies Press. doi:10.17226.18722.
4 National Academies of Sciences, Engineering, and Medicine. (2019). Fostering the Culture of Convergence in Research: Proceedings of a Workshop. Washington, DC: The National Academies Press. doi:10.17226/25271.
5 National Research Council. (2015). Enhancing the Effectiveness of Team Science. Washington, DC: The National Academies Press. doi:10.17226/19007.
Stokols explained that the 2015 report defines the field of team science as an interdisciplinary field concerned with understanding and managing the circumstances that facilitate or hinder the effectiveness of cross-disciplinary research, training, and translational initiatives. The literature on team science has grown significantly since 2006 and provides a vast toolbox of practical strategies for improving the effectiveness of collaborative research. When considering measurement, team science offers strategies for evaluating both interdisciplinary and transdisciplinary contributions. When using bibliometric measures to gauge team productivity and impact, supplementary qualitative data are generally needed to understand the catalytic impact of the ideas generated by research teams. A team may produce many bibliometric outputs for example, but the incremental significance of each publication may be low compared with the significance of a groundbreaking publication. Interdisciplinary and transdisciplinary scholarly contributions may represent somewhat different categories of convergence in that transdisciplinary collaborations by definition entail deeper integration across multiple fields and more novel or catalytic breakthroughs. A wide range of nuance and measures needs to be considered when measuring different forms of convergent research. The field of collaborative transdisciplinary scholarship and practice provides an array of tools and metrics to measure the different facets of the convergence ecosystem.
Stokols listed several practice resources for team science such as the National Institutes of Health (NIH) National Cancer Institute (NCI) Team Science Toolkit. The University of California, Irvine (UCI) considers “team scholarship” to include fields outside of STEM and provides resources through its Team Scholarship Acceleration Lab. The NIH NCI Field Guide led by Michelle Bennet and her colleagues is a useful hands-on toolkit that provides strategies for effective collaboration in team research settings. Michigan State University developed the Toolbox Dialogue Initiative, which aims to bridge the philosophical differences that may arise among research team members. Finally, the Swiss Academies of Arts and Sciences Toolbox addresses an array of strategies to advance and measure the coproduction of knowledge in and beyond academia.
Stokols described two important facets of cross-disciplinary collaboration and convergence: (1) fostering intellectual synergy and diversity of thought, and (2) promoting a social integration of research teams and centers resulting in mutual coordination over several phases. The field must consider how to promote each of these facets in the context of the macrosocietal forces that affect the problems that researchers aim to solve. Prior to the COVID-19 pandemic, for example, practitioners understood proximity and environmental supports to be necessary for interpersonal collaboration. Stokols posed a question: How will the reduction of co-location during the COVID-19 era affect the convergent capacity of research teams
and centers, and what new competencies of convergent research will become imperative for carrying out research? Attention should furthermore be paid to the potential effects on training in convergence that may have shifted during the pandemic.
Stokols continued by providing examples of environmental supports for convergence. He encouraged brainstorming exercises that incentivize divergent thinking, which can be adapted for online use. Although some aspects of convergent research can accommodate remote settings, Jason Owen-Smith and colleagues find that co-location in the same building and overlapping activity zones (coffee rooms, break areas) contribute to the likelihood of effective collaboration and of winning cross-disciplinary grants. Research teams and centers often highlight and share their collaborative identities in shared spaces by posting photographs of team members, conference posters, and certificates of collaborative awards. Using shared physical spaces to communicate collective values reinforces collaboration and convergent behaviors. The reduced availability of shared spaces and in-person meetings must be considered as team scientists adapt to the pandemic, during which remote collaboration has become increasingly common.
Stokols outlined four core competencies that convergence training seeks to produce. Convergence must be transdisciplinary, team-based, translational, and transcultural. A normative competence instilled in people who are engaged in the coproduction of knowledge and translational work guides allocation of resources and effort. The coproduction of knowledge may be particularly complex in international collaborations, as the additional political and cultural differences may inhibit convergence.
To close the presentation, Stokols asked attendees to consider the first four National Academies’ consensus reports on convergence: Facilitating Interdisciplinary Research6 (2005); Facilitating Transdisciplinary Integration of Life Sciences, Physical Sciences, Engineering, and Beyond (2014); Enhancing the Effectiveness of Team Science (2015); and Fostering the Culture of Convergence in Research (2019). He advised the attendees to consider what questions they would like to see explored in the next report: Will there be standard or context-dependent definitions and criteria for convergence. Will a typology of convergence emerge where some forms may include a coproduction of knowledge among academic and nonacademic partners? How will competencies for convergence shift in the COVID-19 era? Finally, can we transcend spatial, sociocultural, and geopolitical constraints on convergence?
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6 National Academy of Sciences, National Academy of Engineering, and Institute of Medicine. (2005). Facilitating Interdisciplinary Research. Washington, DC: The National Academies Press. doi:10.17226/11153.
DISCUSSION
Klein asked how Stokols’ work informs the tension between openness and closure, and what implications might follow from the Steering Committee’s definition of convergence. Stokols suggested that if convergence is viewed as an umbrella concept that encompasses interdisciplinarity, transdisciplinarity, community engagements, and the coproduction of knowledge, then the creation of a global, standardized measure of convergence of either the quantitative or qualitative variety will be challenging. A taxonomy of convergence may be more realistic, and certain facets of convergence may be emphasized within different research initiatives or collaborations. Convergence involves several interrelated dimensions, and Stokols stressed the difficulty inherent in imposing a single standard definition. Within a single research team, the measure of outputs will change based on the research phase. In the initial phase, researchers focus on creating new ideas and collaborative processes, so bibliometric and other outputs are few. In later years of the collaboration, researchers begin to generate products, but the number and timing of those vary according to different types of teams. Scholarly teams, for example, may seek new knowledge, the value of which depends on qualitative analysis and appraisal. This type of output differs substantially from that of a manufacturing or corporate team. It is often more difficult and takes more time to gauge the novelty and value of intellectual outputs created by scholarly teams than it does to assess quantity or utility of products generated by nonacademic teams.
Holbrook asked whether a plurality of definitions complicates the prospects for measurement. Stokols responded that measuring a complex phenomenon requires confrontation and identification of its dimensions. In his view, the various facets of convergence at multiple levels must be measured, an approach that is facilitated by the ecosystem model.
Erin Leahey (University of Arizona) asked about the relationship between diversity and the difference between divergent and convergent thinking. Stokols believes the two topics are closely related. To be productive, effective, and creative, a research team must first leverage the diversity of thinking within itself. Research teams and centers may become preoccupied with obtaining and renewing funding and thus may fail to allot adequate time for brainstorming and divergent thinking. Stokols emphasized that this kind of work should exist in a running dialectical exchange with other components of convergent thought such as efforts to integrate fields by bringing research groups together to emphasize common ground. Referring to Klein’s question about navigating the relationship between diversity and the differences between divergent and convergent thinking, Stokols suggested that the variety of categories of diversity (skills, knowledge, and culture) enhance diversity of thinking. The correspondence between the diversity of
a team’s composition and the divergence of the ideas it produces may not be one-to-one, but they are positively correlated—provided that the team’s diversity is managed effectively to avoid fissures (or “faultlines”) that can develop between team members from different backgrounds.
Khargonekar asked whether lessons learned from the measurement of ecosystems might inform the Steering Committee’s deliberations about how to move convergence research forward. Stokols suggested that systematic thought across multiple scales is one core competency for convergent work. Many methodologies are available for ecosystem measurement, but a fuller discussion, he said, falls outside the scope of this convergence workshop.
Entwisle asked Stokols to imagine a potential typology of convergence. Stokols explained that such a typology would have many organizing dimensions such as the extent to which academic fields and nonacademic cultures are involved in the coproduction of knowledge; the question of whether the desired outcome relates to policy making, a medical product, or scientific knowledge; and the potential for near- or long-term applicability. Some convergent work may have results that can immediately be translated into policy, whereas other research may be intellectually novel and catalytic but not have immediate policy implications.
