Policy Integration and Multi-Level Governance: Dealing with the Vertical Dimension of Policy Mix Designs
Bibliographic record
Abstract
Multifaceted problems such as sustainable development typically involve complex arrangements of institutions and instruments and the subject of how best to design and operate such ‘mixes’, ‘bundles’ or ‘portfolios’ of policy tools is an ongoing issue in this area. One aspect of this question is that some mixes are more difficult to design and operate than others. The paper argues that, <em>ceteris paribus</em>, complex policy-making faces substantial risks of failure when horizontal or vertical dimensions of policy-making are not well integrated. The paper outlines a model of policy mix types which highlights the design problems associated with more complex arrangements and presents two case studies of similarly structured mixes in the areas of marine parks in Australia and coastal zone management in Europe—one a failure and the other a successful case of integration—to illustrate how such mixes can be better designed and managed more effectively.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".