The parameters of policy portfolios: verticality and horizontality in design spaces and their consequences for policy mix formulation
Bibliographic record
Abstract
Policies increasingly come in complex packages and understanding the nature of design criteria for such portfolios is increasingly important. However, existing studies of policy mixes fail to carefully define the dependent variable of the inquiry. As a result, theorization of policy design has lagged, the cumulative impact of empirical studies has not been great and understanding of the phenomena, despite many observations of its significance in policy studies, has not improved significantly over the past three decades. This paper aims to revitalize this important aspect of policy design work and policy studies by distinguishing between mix types and their impact on policy formulation. It defines key types and subtypes of mixes based on the complexity of design variables such as the number of goals, the number of policies and the number of levels of government and sectors involved in the design of a policy bundle. The taxonomy is then used to assess the validity and applicability of oft-cited but under-examined portfolio design principles and precepts.
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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 | 0.023 | 0.081 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.016 | 0.019 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| 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".