Types of learning and varieties of innovation: how does policy learning enable policy innovation?
Bibliographic record
Abstract
Policy innovation is considered important for addressing major challenges such as climate change and the sustainable energy transition. Although policy learning is likely to play a key role in enabling policy innovation, the link between them remains unclear despite much research on both topics. To address this gap, we move beyond a binary treatment of policy innovation and differentiate policy problem innovation from policy instrument innovation and policy process innovation. Subsequently, we synthesise the literature on policy learning with the research on the multiple streams framework (MSF), a well-known lens for explaining policy innovation. Like earlier policy learning studies, we distinguish several types of learning by posing the key questions of learning, but in the context of each stream of the MSF: who learns (actors), what (beliefs), how (modes), and to what effect (ripening). This new conceptualisation clarifies the relationship of each type of policy learning to the varieties of policy innovation. Further, it indicates that policy learning is likely to result in policy innovation if and only if it influences the coupling among the three streams during a window of opportunity – through policy entrepreneurship – and not otherwise. We conclude with the implications of this study for future research on policy innovation, policy learning, and the MSF.
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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 | 0.014 | 0.053 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.003 | 0.022 |
| Scholarly communication | 0.014 | 0.025 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 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".