Bibliographic record
Abstract
This commentary is about the ways in which research can enhance policy making. It opens with a broad discussion of the relationship between research and policy as many have traditionally conceived it, often through the metaphor of a policy cycle into which research is inserted at various points of the cycle. The idea of a policy cycle regards the relationship between research and policy in epistemic terms and arguably represents not a description of how policy is in fact made but stands as a rational reconstruction of the policy process. From here, we move to a more socio-psychological approach to how policy is made and how it is that research is used. Flowing from this, I offer ideas for how researchers can establish effective working relations with policy makers, especially those in governments. In the second half of the paper, some of these ideas are illustrated through the Metropolis Project with a focus on its deployment in Canada, which attempted several innovations in this regard, followed by some reflections on the International Metropolis Project.
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.
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 | 0.021 | 0.139 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.017 | 0.016 |
| Scholarly communication | 0.010 | 0.013 |
| Open science | 0.011 | 0.006 |
| Research integrity | 0.109 | 0.088 |
| Insufficient payload (model declined to judge) | 0.009 | 0.007 |
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".