Bibliographic record
Abstract
Abstract After the arrests of a number of prominent women traffickers in 1975, the Federal Bureau of Investigation proclaimed that there was ‘no anti‐woman bias’ in the Latin American drug trade ( New York Times , April 22, 1975). Long before, narcotics warriors in the US, Mexico, and Canada had monitored the activities of a number of prominent women traffickers. Using official documents, reports, and newspapers, this article examines five cases of women smugglers of opiates and marijuana who operated from the early 1910s to the 1960s. Two of the women lived in the US, two in Mexico, and one in China. For all of them, Mexico served as a source of supply, a site of transit, a point for contacts, and/or a place for peddling. In this study, I argue that women found lucrative opportunities in the trafficking of narcotics just as men. Moreover, this study challenges the masculine constructions of the narcotics trade by considering how female peddlers used certain spaces in the economy to develop their enterprises or how they exploited certain gendered stereotypes to undermine the law.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.004 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".