New pathways to paradigm change in public policy: combining insights from policy design, mix and feedback
Bibliographic record
Abstract
To tackle the manifold crises of our times, most notably the environmental crises we face, ambitious policy change is urgently needed to achieve the necessary radical transformation of our industrialised societies. Yet, while there is increasing demand for public policy scholarship to provide guidance on how policy should be designed to achieve such change, existing scholarship struggles to provide ‘forward-looking’ recommendations. Within this context, our article takes a step back to reconsider the underlying logics of policy change. We argue that focusing on policy, its effect and the subsequent politics it triggers is best achieved by combining insights from the policy design, policy mix and policy feedback literatures. This combination allows us to re-evaluate which potential pathways towards policy change exist. The main contribution of our article is its proposition of two distinct pathways towards policy change, building on a systematic understanding of policy design elements. These pathways place greater emphasis on policy change happening (1) ‘bottom-up’ through initial low-level design changes rather than ‘top-down’ through high-level ideational change, as argued in earlier scholarship, (2) through the interplay of several policies in a complex mix. In this way, these pathways provide a useful framework for systematically analysing how policy should be designed to achieve ambitious policy change and thus enable transformative societal change.
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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 | 0.053 | 0.053 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.007 | 0.060 |
| Scholarly communication | 0.034 | 0.055 |
| Open science | 0.004 | 0.020 |
| Research integrity | 0.008 | 0.012 |
| Insufficient payload (model declined to judge) | 0.008 | 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".