Bibliographic record
Abstract
This chapter explores the motives and processes for regulatory cooperation within North America, primarily between the United States and Canada, in the context of variable, often “dual-bilateral” integration. It examines regulatory cooperation as a continuum of different approaches balancing varying degrees of cooperation or regulatory emulation across economic and policy sectors with differences in national (and sub-national) regulatory institutions and priorities. It also summarizes the history and evolving motivations of central governments that have encouraged and limited regulatory cooperation in the twenty-first century, different sectoral and product-specific patterns of cooperation, along with institutional drivers and constraints that shape its nature and extent during the 2020s. It finds that much of the rhetoric surrounding international regulatory cooperation, whether aspirational or alarmist, far exceeds the actual outcomes of efforts to achieve cooperation and/or mutual recognition of national – generally sector-specific – regulatory processes. The most common form of bilateral (US–Canada) regulatory cooperation is incremental regulatory change led by agencies with established habits of cross-border and wider regulatory cooperation driven by above-average levels of operational interdependence.
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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.015 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".