Bibliographic record
Abstract
On 30 September 2018, the US, Canada and Mexico announced they had reached a trilateral free trade agreement in the renegotiation of the NAFTA, concluding more than 13 months of negotiations. The USMCA has been ratified by all three countries and has taken effect as of 1 July 2020. One of its crucial characteristics when it comes to workers’ rights is a facility-specific rapid response labour mechanism, which purpose is to ensure remediation of a “Denial of Rights” of free association and collective bargaining for workers at a Covered Facility, and to ensure that remedies are lifted immediately once a Denial of Rights is remediated (Annex 31-A “Facility-specific rapid response labor mechanism” between the US and Mexico of the USMCA’s Dispute Settlement chapter and separate Annex 31-B between Canada and Mexico). From 2021, the above-mentioned mechanism has been already used by the Department of Labor and the Office of the US Trade Representative on many occasions in order to protect workers’ rights under the USMCA. The author of this paper examines six first cases, namely: GM Silao, Tridonex, Panasonic, Teksid, VU Manufacturing and Saint Gobain. On such basis she draws conclusions regarding the effectiveness of the facility-specific rapid response labour mechanism.
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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 | 0.023 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".