Towards an agnotology of policy studies: identifying, understanding and addressing knowledge limitations in real world policymaking
Bibliographic record
Abstract
Background: This comment responds to points raised in Hannah et al (2023). Aims and objectives: The commentary frames the Hannah et al discussion within other recent moves in the policy field to take ‘non-knowledge’ more seriously. Methods: The commentary situates the Hannah et al discussion within the traditional literature on knowledge utilisation in the public policy literature. Findings: It is argued that while the Hannah et al article is an advance in thinking in the field it does not deal adequately with earlier efforts and findings in the literature. Discussion and conclusion: More work towards ‘An agnotology of the policy studies’ is needed.
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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 | 0.247 | 0.434 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.015 | 0.120 |
| Scholarly communication | 0.029 | 0.052 |
| Open science | 0.013 | 0.017 |
| Research integrity | 0.064 | 0.074 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".