Bibliographic record
Abstract
In the mid 1950's, they began hiring braceros to help with the harvesting of the cotton. Ms. Ramos married Benito Juarez in 1945; her husband owned a ranch in Delmita, Texas that had been in his family for several generations; although her parents were migrant workers, she did not begin ranching until shortly after getting married; she and her husband knew about the braceros because they would often come to work in the neighboring city of Edinburg, Texas; in the mid 1950s, they began hiring braceros to help during the cotton season; they would hire between eighteen and twenty workers to help with the harvesting of the cotton; Laurentina recalls that most of the workers were between the ages of twenty and forty; the braceros would stay in the old abandoned house that belonged to Benito’s parents; although there were no beds in the house, the workers were given plenty of blankets and a radio for entertainment; they would use the bathrooms and washing machines in the main house; oftentimes, the braceros were passed on to her brother-in-law, and they would help him on his ranch; she would interact with the braceros often, as she would weigh the cotton they picked; in addition, she goes on to describe what some of the braceros were like in general and specific memories she has of them.
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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 | 0.005 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.139 | 0.046 |
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".