Compliance with International Treaties: Selective Adaptation Analysis
Bibliographic record
Abstract
Summary This comment revisits the debate on the reasons for compliance, or lack thereof, with the regulations and administrative rules that govern the current international trade regime. The research on which it is based is the first part of the five-year project on Cross-Cultural Dispute Resolution funded by the Major Collaborative Research Initiative program of the Social Sciences and Humanities Research Council of Canada. It focuses on the cultural components of non-compliance based on analysis of the legislative internalization of World Trade Organization norms and case law in China, Canada, and Japan on the one hand and on individual perceptions of the international trade environment on the other. The main hypotheses are that the sharing of international practice rules does not necessarily indicate consensus on the normative order underlying those rules and that the behaviour of those who are involved in the interpretation and application of international rules is informed by (1) their perception of the purpose, content, and effect of nonlocal rules and their underlying norms; (2) those rules' and norms' complementarity with local rules and norms; and (3) the degree of legitimacy accorded by local communities to the processes of interpretation and application.
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 | 0.010 | 0.055 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".