Third Generation Policy Diffusion Studies and the Analysis of Policy Mixes: Two Steps Forward and One Step Back?
Bibliographic record
Abstract
Three features of Gilardi and Meseguer's recent announcement of the start of a “third generation” of diffusion research in Europe require evaluation. First, conceptualization of policy diffusion is considered a task completed by the first two “generations”. Second, the work of the second generation is located against the background of globalization, democratization and the trend towards the adoption of market instruments. And, third, methodological sophistication is equated with the development of large-n empirical methodologies. Each of these features is discussed in turn. We argue that diffusion studies remain seriously hindered by a lack of clarity about the dependent variable under examination; second, that the peculiar interest of the second generation in measuring the impact of large scale diffusion mechanisms such as democratization, globalization and market orientation has led to an unfortunate focus on the adoption of particular instruments and “settings” as the sole indicators of diffusion; and third, that when we expand “what” is being diffused to include policy goals and objectives, advancing beyond the second generation requires a more plural methodological framework sensitive to context, including both the thick descriptions and the comparative small-n case studies which were a feature of earlier “first” and “second” generation studies. These points are illustrated with contemporary examples involving the development and diffusion of new “integrated” and “coherent” mixes of regulatory and market instruments in the form of Integrated Coastal Zone Management (ICZM) in Europe.
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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 | 0.035 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.011 | 0.016 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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 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".