Inclusion of gender and labour standards in preferential trade agreements: evidence from North American and Canada-Chile agreements
Bibliographic record
Abstract
This article analyses the causes of the incorporation of gender provisions into preferential trade agreements (PTAs), based an analysis of the reasons for the inclusion of both labour and gender provisions in the North American Free Trade Agreement (NAFTA) and the United States-Mexico-Canada Agreement (USMCA), and in the Canada-Chile Free Trade Agreement (CCFTA).Drawing upon feminist and constructivist analysis, it argues for the importance of ideational factors such as policy emulation, social learning, the formation of transnational advocacy networks (TANs), as well as the role of Global South actors.Gender provisions often contain neoliberal elements that promote female entrepreneurship rather than more transformational approaches.The comparison of the USMCA and the CCFTA shows that the inclusion of stronger gender provisions in labour chapters (as in the USMCA) may be a more effective way to promote the interests of vulnerable workers than the inclusion of a gender chapter.
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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 | 0.009 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.011 | 0.011 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".