The Role of Institutions and Capital in Intersectoral Collaboration: Infection and Immunity Research and Development Collaboration in <scp>V</scp>ancouver
Bibliographic record
Abstract
Abstract Collaborations between and within sectors are common and crucial to the creation and transfer of knowledge. It is often unclear who is involved in the collaboration, and with whom and why they are collaborating. I studied reasons for collaboration and how capital and institutions affect collaboration through a mixed methods analysis of infection and immunity research and development collaborations in Vancouver, Canada between individuals affiliated with universities, firms, and health‐care organizations. I found that both capital and institutions were important in collaboration decisions. Collaboration worked as a balancing act between capital and institutions. Potential collaborators needed to offer different capital to the collaboration while supporting the dominant institutions of potential collaborators. Participants' organizational and sectoral affiliations influenced available capital and dominant institutions. These findings help policy makers understand collaboration dynamics between sectors and how translation can occur between universities, firms, and health‐care organizations.
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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 | 0.038 | 0.059 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.009 | 0.010 |
| Scholarly communication | 0.016 | 0.005 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.001 | 0.001 |
| 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".