Bibliographic record
Abstract
Mr. de la Cruz briefly discusses his family and childhood; in 1954, the family moved to a place just outside of Empalme, Sonora, México, where he learned about the bracero program; he could not find work in México at the time, and he decided to enlist in the program; to begin the process, he went to Empalme to pick cotton and get a card and his name on the list of available workers; from there, he had to wait to be called, which could happen anywhere from ten days to one month; he describes waiting in line to be fumigated and the other medical procedures he underwent; his first contract took him to Holtville, California, where he worked in the beet fields for forty-five days; he then went to Palo Verde, Arizona, for three months, to pick onions and potatoes and load trucks, which was especially difficult; later, he returned to the beet fields, but this time in Burbank, California; he explains how he used the short hoe, and he stayed there for three months; in addition, he went to Suisun [City] Arizona, for a short time, and picked tomatoes; his last contract took him to Salinas, California, for one month, to pick strawberries; while there, he worked between ten and twelve hours a day; he would send money home to his parents and wife as often as he could; when he first started working in California, he earned 80¢ per day, but he had to pay $3.00 per day for food; in Arizona, he earned $1.25 per hour, and the work was easier; he goes on to describe his life after the bracero program ended; he eventually emigrated to the United States with his wife.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.261 | 0.073 |
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".