Network relationships and standard adoption: Diffusion effects in transnational regulatory networks
Bibliographic record
Abstract
Abstract The soft law measures that transnational regulatory networks produce have become increasingly important in regulating cross‐border market activity. However, domestic agencies vary considerably in terms of the rate by which these soft law measures are adopted, and the ways in which they spread across jurisdictions are not well understood. This article argues that existing theoretical explanations referring to socialization or power dynamics have a specific network‐structural pattern associated with them, and that longitudinal network analysis can be used to test their hypothesized effects. In particular, we study the widespread adoption of the International Organization of Securities Commissions’ (IOSCO) Multilateral Memorandum of Understanding (MMoU). Based on a longitudinal dataset (2002–15) of the inter‐agency relationships between securities regulators (n = 109), we use Stochastic Actor‐Oriented Models (SAOM) to predict the rate at which transnational standards are adopted by domestic agencies. The results indicate that standard adoption is contagious in the network of securities regulators.
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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 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".