Framework or metaphor? Analysing the status of policy learning in the policy sciences
Bibliographic record
Abstract
Recently, it has been argued that the many works on policy learning constitute a stand-alone basis for understanding policy processes. In this study, we evaluate this claim through a bibliometric analysis of 588 publications on the topic in the Web of Science database, complemented by a literature review. We find that while the study of learning is supported by an active and growing research community, it has neither definitional clarity nor a shared vocabulary. And, further, its model of agency is both incomplete and inconsistent. As such, the subject remains more a metaphor than a framework of analysis, per se, and has little potential to advance epistemologically. Given this analysis, we argue that intellectual resources are better spent organising research on learning within existing frameworks rather than attempting to create a new stand-alone one that would contribute to the further splintering of an already fragmented field of study.
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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 | 0.042 | 0.101 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.023 | 0.038 |
| Science and technology studies | 0.004 | 0.036 |
| Scholarly communication | 0.024 | 0.032 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".