Bibliographic record
Abstract
Although NAFTA stimulated transnational relationships among key union federations and industrial unions by providing a new transnational political opportunity structure that constituted North American actors and interests, not all industrial unions were transformed by its effects. The naysayers who predicted NAFTA would make cross-border collaboration impossible were not completely incorrect. Indeed, the trade agreement did little to generate transnational relationships among U.S. and Canadian unions in the auto, garment/apparel, and trucking industries and their counterparts in official Mexican unions. In addition, the free trade agreement created a rift in the uninspired though cordial relations among the AFL-CIO, CLC, and CTM, and generated significant contention between the Teamsters and CTM trucking unions. Rather than constitute them as regional actors, NAFTA actually underscored the national interests of unions in these industries and generated intense nationalistic sentiments and strategies. In NAFTA's wake, the majority of these unions had some contact and interaction. For most, contact was minimal and interaction intermittent. Labor leaders expressed little if any reluctance to admit poor relations with their counterparts. Ironically, their frankness in revealing this unflattering side of the history, and their unwillingness to paint a rosy picture of harmonious post-NAFTA relationships when they did not exist, strengthens the validity of the data in other chapters that demonstrate NAFTA's catalytic effect on labor relationships.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.034 | 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".