Indicators at the interface: managing policymaker-researcher collaboration
Bibliographic record
Abstract
The knowledge transfer literature encourages partnerships between researchers and policymakers for the purposes of policy-relevant knowledge creation. Consequently, research findings are more likely to be used by policymakers during policy development. This paper presents a set of practice-based indicators that can be used to manage the collaborative knowledge creation process or assess the performance of a partnership between researchers and policymakers. Indicators for partnership success were developed from 16 qualitative interviews with health policymakers and researchers involved with eight research transfer partnerships with government. These process and outcomes indicators were refined through a focus group. Resulting qualitative and quantitative indicators were judged to be clear, relevant, credible, and feasible. New findings included the need to have different indicators to evaluate new vs mature partnerships, as well as specific indicators common to researcher-policymaker partnerships in general.
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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 | 0.223 | 0.409 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.017 | 0.017 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.016 | 0.026 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".