Patching vs Packaging in Policy Formulation: Assessing Policy Portfolio Design
Bibliographic record
Abstract
Thinking about policy mixes is at the forefront of current research work in the policy sciences and raises many significant questions with respect to policy tools and instruments, processes of policy formulation, and the evolution of tool choices over time. Not least among these is how to assess the potential for multiple policy tools to achieve policy goals in an efficient and effective way. Previous conceptual work on policy mixes has highlighted evaluative criteria such as "consistency" (the ability of multiple policy tools to reinforce rather than undermine each other in the pursuit of individual policy goals), "coherence" (or the ability of multiple policy goals to co-exist with each other in a logical fashion), and "congruence" (or the ability of multiple goals and instruments to work together in a uni-directional or mutually supportive fashion) as important design principles and measures of optimality in policy mixes. And previous empirical work on the evolution of existing policy mixes has highlighted how these three criteria are often lacking in mixes which have evolved over time as well as those which have otherwise been consciously designed. This article revisits this early design work in order to more clearly assess the reasons why many existing policy mixes are sub-optimal and the consequences this has for thinking about policy formulation processes and the practices of policy design.
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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 | 0.076 | 0.190 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.012 | 0.019 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.003 |
| 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".