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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.005 |
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; both teacher heads agree on what is shown here.
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".