Bibliographic record
Abstract
Abstract This chapter examines how labour provisions in trade agreements can drive reforms in countries’ domestic labour law systems. It first analyses reforms that occur during the negotiation stage and later under enforcement mechanisms. Case studies include US trade agreements beginning with NAFTA in 1994; post-NAFTA trade pacts that followed; trans-Pacific agreements; and EU agreements with Canada, Vietnam, Korea, and others. Special attention is paid to the North American USMCA trade agreement, which brought far-reaching labour law reforms in Mexico and whose unique ‘rapid response’ enforcement mechanism fosters independent union organizing. In conclusion, this chapter cautions that the question of causality is nuanced, and that trade negotiations can promote labour reforms only with pressure from other actors as well. These include national and global trade unions, political parties, ILO, and civil society advocates. Most important, trade-labour negotiations and enforcement must accompany efforts by workers to exercise rights that labour chapters in trade agreements seek to protect.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.011 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.023 | 0.003 |
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".