DARE to be different? A novel approach for analysing diversity in collaborative research projects
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.054 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.016 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it