Trumping Capacity Gap with Negotiation Strategies: the Mexican USMCA Negotiation Experience
Bibliographic record
Abstract
ABSTRACT In the past few months, we have witnessed the ‘worst deal’ in the history of the USA become the ‘best deal’ in the history of the USA. The negotiation leading to the United States–Mexico–Canada Agreement (USMCA) appeared as an ‘asymmetrical exchange’ scenario that could have led to an unbalanced outcome for Mexico. However, Mexico stood firm on its positions and negotiated a modernized version of North American Free Trade Agreement. Mexico faced various challenges during this renegotiation, not only because it was required to negotiate with two developed countries but also due to the high level of ambition and demands raised by the new US administration. This paper provides an account of these impediments. More importantly, it analyzes the strategies that Mexico used to overcome the resource constraints it faced amidst the unpredictable political dilemma in the US and at home. In this manner, this paper seeks to provide a blueprint of strategies that other developing countries could employ to overcome their negotiation capacity constraints, especially when they are dealing with developed countries and in uncertain political environments.
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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 | 0.010 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.009 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".