Growing a University policy engagement function; Towards better models, methods, and measures of success
Bibliographic record
Abstract
This report identifies current and recent effective practice in policy engagement from a university setting. It is intended to provide a suite of options for academic-policy engagement activities and processes that we hope is useful for any university seeking to grow a university policy engagement function. We hope that it can support universities who are exploring potential models for university policy engagement centres.It is aimed at any university leader, academic, funder, or professional support officer seeking to set up a new centre or expand an existing one. The focus is predominantly UK – but we learned important insights from interviews with experts in Australia, Canada, the EU, and the US.We recognise that this report sits within a wider discussion currently underway within higher education and its core purposes across research; teaching and learning; and engagement, and the pressing nature of financial considerations in delivery and planning for universities.
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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 | 0.305 | 0.291 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.015 | 0.022 |
| Science and technology studies | 0.011 | 0.027 |
| Scholarly communication | 0.059 | 0.060 |
| Open science | 0.007 | 0.029 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".