Procedures Adopted in the Rapid Response Labour Mechanism of the United States–Mexico–Canada Agreement: The Case Study Research and Lessons for the European Union
Bibliographic record
Abstract
The aim of this article is to discuss a sanction-based model of labour provisions in free trade agreements (FTAs) adopted by the United States, against the background of the EU “promotional model” focusing on dialogue and cooperation. Using the case study research method, the authors analyses the United States–Mexico–Canada Agreement (USMCA) cases and the procedures adopted in the Rapid Response Labour Mechanism (RRLM). The findings of this paper reveal, among other things, that most USMCA cases were successful. The article shows that the EU should enter FTA negotiations with hard provisions in the templates on the table and should stick to both the pre-ratification and post-ratification conditionality which could increase the enforceability of labour provisions in FTAs.
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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 | 0.020 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.015 | 0.018 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".