Labor Violations in Mexico: Can New Trade Agreements Effectuate Change?
Bibliographic record
Abstract
Child labor and forced labor remain pervasive problems on Mexican farms. Millions of workers on these farms are forced to work and live in inhumane conditions, only to leave the season’s harvest just as poor as they were before. To date, human rights and labor treaties and agreements that Mexico is party to have failed to protect workers. In early 2016, however, negotiations on the Trans-Pacific Partnership (“TPP”) concluded and, if ratified, the party-countries claim that the TPP will hold Mexico to higher standards than previously faced because the TPP will link labor rights with trade law. However, this was the hope when Mexico, Canada, and the United States placed the North American Free Trade Agreement (“NAFTA”) into force as well. This article will therefore analyze whether the TPP is indeed an improvement on NAFTA and, if so, whether the TPP will work to effectively enforce Mexican labor rights. This article begins with a look at the violations occurring on the farms, followed by a summary of the international human rights laws, international labor laws, and international trade laws that Mexico is already party to. The article also includes an in-depth summary of the labor side-agreement to NAFTA, the North American Agreement on Labor Cooperation and how this side agreement compares to the TPP. Lastly, this article examines additional efforts that may be made to uphold labor rights on Mexican farms, including a bottom-up approach that involves both laborers and consumers. Accordingly, this article concludes that the TPP does represent a significant improvement on NAFTA, though it remains to be seen whether this improvement will itself be enough to effectuate change in Mexico.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 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".