Regionalism and diffusion revisited: From final design towards stages of decision-making
Bibliographic record
Abstract
Abstract An emerging research programme on diffusion across regional international organisations (RIOs) proposes that decisions taken in one RIO affect decision-making in other RIOs. This work has provided a welcome corrective to endogenously-focused accounts of RIOs. Nevertheless, by focusing on the final design of policies and institutional arrangements, it has been conceptually overly narrow. This has led to a truncated understanding of diffusion’s impact and to an unjustified view of convergence as its primary outcome. Drawing on public policy and sociological research, we offer a conceptual framework that seeks to remedy these weaknesses by disaggregating the decision-making process on the ‘receiving’ side. We suggest that policies and institutional arrangements in RIOs result from three decision-making stages:problematisation(identification of something as a political problem),framing(categorisation of the problem and possible solutions), andscripting(design of final solutions). Diffusion can affectanycombination of these stages. Consequently, its effects are more varied and potentially extensive than is currently recognised, and convergence and persistent variation in scripting are both possible outcomes. We illustrate our framework by re-evaluating research on dispute settlement institutions in the EEC, NAFTA, and SADC. We conclude by discussing its theoretical implications and the conditions that likely promote diffusion.
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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 | 0.049 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.059 |
| Scholarly communication | 0.014 | 0.015 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".