Bibliographic record
Abstract
Mr. Hernández recalls that sometime during the late fifties or early sixties, he enlisted in the bracero program, and he went through a contracting center in Monterrey, Nuevo León, México; he comments that he went with one of his brothers-in-law, but they were separated during the x-ray portion of their processing; they did, however, meet up later while in the United States, and they picked cotton together; as a bracero, Dionisio labored in the fields of California and Texas until the program ended in 1964; he goes on to briefly recount the different worksites, housing, payment, treatment, and recreational activities; in addition, he describes working in Lamesa, Texas, and having to prepare his own meals, which was especially difficult, because he did not know anything about cooking; he also saw snow for the first time while in Texas; moreover, he explains that Hidalgo, Texas was the worst; there were about three hundred braceros there, and they were divided into smaller working groups; sometimes, they were not taken to the fields until 10:00 AM or 11:00 AM, at which point there was not much left to pick; eventually, by the early sixties, he had saved enough money to buy a car; he also explains that a group of braceros organized to demand better pay and appointed him their leader; in the end, they all backed out, but he refused to continue working for little pay, so he returned to México; he married in 1970, and a year later his wife had their first child; by 1972, he and his family moved to Manuel Doblado, Guanajuato, México, and settled there; his overall memories of the program are positive.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.267 | 0.076 |
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".