LeClercq on potential international trade changes under new Trump administration
Bibliographic record
Abstract
Assistant Professor Desirée LeClercq offers insight on potential international trade changes under the new Trump administration. The Biden administration used a trade agreement negotiated under the first Trump administration, the United States-Mexico-Canada Agreement (USMCA), to empower workers in the trade sector in Mexico through targeted enforcement. Will President-elect Trump abandon the “worker-centered” trade agenda of the USMCA? That is unlikely. President Trump negotiated the USMCA to ensure that lax labor rights in Mexico would not offer U.S. companies incentives to move their facilities south of the border. By enforcing the USMCA’s labor rights in Mexico, the Biden administration protected workers in Mexico while, incidentally, achieving Trump’s objective to equalize production costs in the two countries. If anything, the second Trump term will increase enforcement of the USMCA’s labor protections in Mexico. LeClercq’s recent study, published by Cornell University, shows that the Mexican workers who benefitted under USMCA during the Biden administration had connections to U.S. labor unions and NGOs. The Trump administration will likely de-politicize that enforcement and expand it to cover additional Mexican facilities. Doing so ensures that no companies in Mexico benefit from U.S. market access by evading the rules. University of Georgia School of Law Assistant Professor of Law & Faculty Co-Director of the Dean Rusk International Law Center Desirée LeClercq, who specializes in international labor law and worked in the Office of the U.S. Trade Representative during the previous Trump administration, as well as in the International Labor Organization and at the National Labor Relations Board, is available for further commentary at desireelc@uga.edu.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.010 | 0.010 |
| Insufficient payload (model declined to judge) | 0.024 | 0.005 |
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".