DARE to be different? A novel approach for analysing diversity in collaborative research projects
Bibliographic record
Abstract
Abstract Growth in collaborative research raises difficulties for those tasked with research evaluation, particularly in situations where outcomes are slow to emerge. This article presents the ‘Diversity Approach to Research Evaluation’ (DARE) as a novel way to assess how researchers engaged in knowledge creation and application work together as teams. DARE provides two important insights: first, it reveals the differences in background and experience between individual team members that can make research collaboration both valuable and challenging; second, DARE provides early insights into how team members are working together. DARE achieves these insights by analysing team diversity and cohesiveness in five dimensions, building on Boschma’s multi-dimensional concept of proximity. The method we propose combines narratives, maps, and indicators to facilitate the study of research collaboration. The article introduces the DARE method and pilots an initial operationalization through the study of two grant-funded biomedical research projects led by researchers in the UK. Suggestions for further development of the approach are discussed.
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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 | 0.070 | 0.128 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.018 | 0.011 |
| Science and technology studies | 0.007 | 0.017 |
| Scholarly communication | 0.018 | 0.020 |
| Open science | 0.003 | 0.020 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".