11ECONOMIC AND FINANCIAL REVIEW THIRD QUARTER 2001
Bibliographic record
Abstract
Among the most striking industrial phe-nomena in the wake of the North American Free Trade Agreement has been the rapid growth of plants that operate under Mexico’s maquiladora program. In its simplest organizational form, a maquiladora plant imports inputs—typically from the United States—processes them, and then ships them back to the country of origin, perhaps for more processing. The maquiladora program permits the inputs and the machinery to process them to enter Mexico tariff-free. On the goods ’ return, the shipper pays duties only on the value added by manufacture in Mexico.1 Although maquiladoras have operated in Mexico since the 1960s, their output and em-ployment growth began to accelerate markedly with the advent of NAFTA in 1994 (Figure 1). Over the first six years after the onset of NAFTA, maquiladora employment grew 110 percent, compared with 78 percent over the previous six years. NAFTA opponents and supporters as well as others have concluded that the trade agree-ment was the cause of this sharp acceleration. Balla (1998, 55), for example, claims that “with-out doubt, NAFTA has resulted in a dramatic increase in activity in the maquiladora industry.” San Martin (2000, 32A) maintains that “NAFTA continues to drive the growth of the maquila-dora industry. ” Carrada-Bravo (1998, 8) argues that “the acceleration of foreign direct invest-ment under NAFTA also contributed to the crea-tion of more than a half-million new employ-ment opportunities in the U.S.–Mexico border region.…These new jobs, tied to the expansion of the maquiladora industry, [pay more] than those not related to international trade. ” A post-
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.011 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.172 | 0.075 |
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".