Sociocultural Consequences of Free Trade: Accountability for Murder in the Maquiladoras: Linking Corporate Indifference to Gender Violence at the U.S.-Mexico Border
Bibliographic record
Abstract
Claudia Ivette-Gonzalez might still be alive if her employers had not turned her away. The 20-year-old resident of Ciudad Juarez - the Mexican city abutting El Paso, Texas - arrived at her assembly plant job four minutes late one day in October 2001. After management refused to let her into the factory, she started home on foot. A month later, her corpse was discovered buried in a field near a busy Juarez intersection. Next to her lay the bodies of seven other young women. [FN2]The “maquiladora murders” have become a popular subject for writing and activism by feminists, as well as the inspiration for numerous forms of art, [FN3] literary fiction [FN4] and commentaries, [FN5] international conferences, [FN6] movies, [FN7] and marches [FN8] on both sides of the border. A 2004 conference held at the University of California-Los Angeles entitled “Maquiladora Murders” [FN9] drew worldwide attention [FN10] to the cases of hundreds of young Mexican women who worked in maquiladoras - American-owned transnational factories - and met untimely, often brutal deaths. Who killed them is still a mystery. [FN11] What is not a mystery is that incidents of domestic violence and femicide [FN12] in Ciudad Juarez [FN13] have risen in the wake of heavy industrialization along the border; that industrialization was a result of the signing of the 1993 North American Free Trade Agreement (NAFTA) between Mexico, the United States, and Canada.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.011 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".