Bibliographic record
Abstract
Mr. Rosanes briefly talks about his family and what his life was like growing up; for a brief time, he worked in the United States without proper documentation; later, he picked cotton in Sonora, México, to obtain the necessary papers to enlist in Empalme, Sonora, México, where he was medically examined; as a bracero, he labored in the fields of California and Michigan, picking cucumbers, grapes, lemons, oranges and tomatoes; he goes on to detail the worksites, camp sizes, housing, accommodations, amenities, provisions, duties, routines, treatment, friendships, payments and recreational activities, including trips into town; on occasion, Mexican officials visited the camps to ensure adequate treatment; immigration officials also went to the camps regularly, and men without documents often worked alongside the braceros; while in Tracy, California, the men went fishing at a nearby river on their days off; in addition, he explains that he spent the most time working in Ontario, California; his employer arranged to help him obtain legal status, and his visa came through while he was working in Michigan, but he did not claim it; later, through amnesty, he was able to obtain legal status in the United States; overall, he has positive memories of the program, because he was able to have a better life.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.134 | 0.033 |
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".