Introduction: The apparel industry and North American economic integration
Bibliographic record
Abstract
The economic and social consequences of international trade agreements have become a major area of inquiry in development studies in recent years. As evidenced by the energetic protests surrounding the Seattle meeting of the World Trade Organization (WTO) in December 1999 and the controversy about China's admission to the WTO, such agreements have also become a focus of political conflict in both the developed and developing countries. At issue are questions of job gains and job losses in different regions, prices paid by consumers, acceptable standards for wages and working conditions in transnational manufacturing industries, and the quality of the environment. All these concerns have arisen with regard to the North American Free Trade Agreement (NAFTA) and can be addressed through an examination of changes in the dynamics of the apparel industry in the post-NAFTA period.1 In this book, we examine the evolution of the apparel industry in North America in order to address some of these questions as they pertain to North America, with an eye toward the broader implications of our findings. We also consider the countries of the Caribbean Basin and Central America, whose textile and apparel goods are now allowed to enter the U.S. market on the same basis as those from Canada and Mexico (Odessey 2000). © 2009 by Temple University Press. All rights reserved.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.026 | 0.004 |
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".