Introduction: Understanding integrated policy strategies and their evolution
Bibliographic record
Abstract
Abstract Much attention in recent years has been focused on the idea of replacing patchworks of public policies in specific issue areas with more coordinated or ‘integrated’ policy strategies (IS). Such strategies are expected to display a match of coherent policy goals and consistent policy means which can produce policy outcomes optimally matched to specific large-scale problem contexts. Work on such strategies in areas such as Integrated Coastal Zone Management (ICZM), National Forest Policies (NFPs), European transportation and energy planning, Mediterranean desertification and others, however, has shown a remarkable resilience of pre-existing policy elements, leading to policy failures and other sub-optimal outcomes. On the basis of a review of this literature, this article argues that the development of IS typically follows one or more of the processes Thelen et al. have characterized as ‘displacement, conversion, layering, drift and exhaustion’. Studies of IS must take this evolutionary perspective into account in developing a better understanding of issues surrounding appropriate IS 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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".