Bibliographic record
Abstract
Mr. Loza talks about the hacienda where he grew up and how it gradually changed over time, eventually becoming a city; in addition, he explains what it was like living and working on an ejido; in 1942, government officials went to ranches in buses to enlist and take people for the bracero program; he describes the indecision many men faced with regard to joining the program, working on the hacienda, or taking over an ejido; although he never became a bracero, his brother, José, and two sons, Juan and Manuel, did; Cayetano discusses how his sons were able to get on the list of available workers and how they went through the contracting center in Irapuato, Guanajuato, México; he bought livestock for them whenever they sent money home; by the time Juan returned, he had learned to speak and read in English; he eventually went back to the United States, married, and settled there; when Manuel came back, he was more reserved; he had been treated badly while working as a bracero, and he never wanted to return to the United States; Cayetano also discusses how other men left with the program and returned after only a few months; they often came back with clothes and radios as gifts; a number of men he knew died while in the United States; he speculates they became ill and had no one to care for them; Cayetano knew of another man from the surrounding area who was burned while in his room at night; having lived in the same place, Cayetano was able to see a number of men coming and going throughout the duration of the bracero program.
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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 | 0.006 |
| 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 | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.370 | 0.120 |
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; the direct Gemma label and the distilled Codex classifier 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".