NEW FRONTIERS AND DIRECTIONS IN POLICY TRANSFER, DIFFUSION AND CIRCULATION RESEARCH: AGENTS, SPACES, RESISTANCE, AND TRANSLATIONS
Bibliographic record
Abstract
Abstract Policy transfer, diffusion and circulation studies are a fertile ground for innovation in public policy analysis. In a globalized world, where state boundaries are permeable and public policy travels transnationally, the diffusion of policies is what naturally connects domestic to international policy. Te recent surge of publications in the feld consolidated an important and dense body of knowledge. However, afer years of research, there now seems stasis if not stagnation, with relatively little conceptual innovation. In this article we propose to address fresh avenues for future research, considering what needs to be better understood in the policy diffusion phenomenon. Te new frontiers to be explored are not only associated to heuristic dimensions of the feld, but also to empirical dynamics that emerged in the past years. We highlight six new frontiers for policy transfer and diffusion research: (1) the role of the private sector and consultants; (2) internationalization of domestic coalitions; (3) transnational spaces and transfer agents; (4) policy translation; (5) resistance to transfer; and (6) South-South or South-North transfers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.051 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.005 | 0.062 |
| Scholarly communication | 0.024 | 0.058 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.011 | 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".