Diffusion of Regulatory Policy Across Nations: The Example of Number Portability
Bibliographic record
Abstract
Out of around 200 countries in the world, only 75 have number portability. What are the international factors that explain the diffusion of this regulatory policy? Research on policy diffusion offers several explanations: constructivist, coercion, competition, and learning. Each of these theories is explored based on a dataset that tracks the implementation of number portability, fixed phone competition and mobile phone competition, and documentary evidence gathered from the Asia Pacific Economic Cooperation (APEC), COMESA (Common Market for Eastern and Southern Africa, Economic Community of West African States (ECOWAS), European Union, and the Inter-American Telecommunications Commission (CITEL). In these three regulatory issue areas, Asia, Americas, and Europe are the three regions that innovate first; Middle East and Africa follow later on. Further, Hong Kong and New Zealand in Asia and Canada, Chile, and the US in Americas are pioneers, while others wait to see results before proceeding; learning appears to explain the diffusion pattern in these regions. In contrast, in Europe, regulatory diffusion begins early and proceeds rapidly with pioneers like Finland and United Kingdom, but others adopt without the lag time observed in Asia and Americas, very likely because of the leadership and enforcement powers of the European Union, a coercive explanation among member states and a competitive one among non-member states.
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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 | 0.007 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".