Job Creation, Job Destruction, and International Competition: Job Flows and Trade - the Case of Nafta
Bibliographic record
Abstract
This paper is a chapter in our forthcoming monograph, Job Creation, Job Destruction, and International Competition (W.E. Upjohn Institute 2003), and expands on the ideas advanced in Klein, Schuh, and Triest (2003). The chapter is a case study of the impact of the North American Free Trade Agreement (NAFTA) on the U.S. labor market in three industries: textiles and apparel, chemicals, and automobiles. NAFTA significantly altered the trade environment for these industries and contributed to changes in the bilateral export-import structure among the United States, Canada, and Mexico. Our innovation is to examine NAFTA's effect on gross job creation and destruction, the components of change in net employment. Except for a more rapid decline in apparel employment, there is little evidence of NAFTA's having had major effects on either net employment or gross job flows in these industries.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".