Whither convergence? Co-designing convergent research and wrestling with its emergent tensions
Bibliographic record
Abstract
Convergence has emerged as an important paradigm for conducting research that tackles grand societal challenges. It demands deep integration of multiple disciplines for a holistic understanding of the complexity of these challenges. In the last decade, most convergent research efforts have focused on the integration of science, technology, engineering, and mathematics (STEM). However, addressing societal challenges necessitates greater integration of the social sciences in order to bring in critical and reflexive thinking. Design, as a discipline, integrates social science foundations with the creative arts and a strong future orientation, to understand human behaviors and interactions across socio-technical systems. Although design has gained attention at the U.S. National Science Foundation (NSF) as a means of identifying use-inspired research and facilitating cross-disciplinary collaboration, it has not been more widely recognized as a valuable discipline contributing to convergent research. This paper examines design’s role in activating convergence within Multiscale Resilient, Equitable, and Circular Innovations with Partnership and Education Synergies for Sustainable Food Systems (RECIPES), an NSF-funded Sustainable Regional Systems Research Network. RECIPES aims to develop scientific breakthroughs in characterizing the complex challenges surrounding food loss and waste in the U.S., as well as to develop innovative, circular, and socially equitable solutions for reducing and managing wasted food. The network uses design to help infrastructure convergence. Prioritizing authentic whole person engagement among network participants, fostering critical reflection through convergence and divergence cycles, and making space for open-ended inquiries around emergent tensions are vitally important. This article is a reflection on this role, with insights and recommendations for more effectively leveraging design in convergence.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.269 | 0.302 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.011 | 0.050 |
| Scholarly communication | 0.025 | 0.034 |
| Open science | 0.006 | 0.025 |
| Research integrity | 0.007 | 0.011 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".