Indicators at the interface: managing policymaker-researcher collaboration
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
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.
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 | 0.036 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.006 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.021 |
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