Bibliographic record
Abstract
Aida and Tula recall their time working as typists at a bracero processing center in Hidalgo, Texas, during the 1950s; they initially learned about the job through word of mouth; upon being hired, they were contracted with the federal government, and given government classifications; although there were different shifts because the center was open twenty-four hours a day, they worked the morning shift, which lasted eight hours; the girls were allowed to take breaks, and they often brought a sack lunch; the braceros were brought into the center, which was a huge warehouse, and they were taken into holding rooms; they were then brought into a big hall and grouped according to where they came from so that they could stand in line and wait for their information to be taken at the screening station where the girls worked; the braceros would hand a paper to the girls, and they would ask basic biographical questions and type out the answers; the girls would then hand the paper back to the workers so they could take it to the next station; oftentimes, the girls would get bored of asking the same questions, and they would ask about the men’s scars or wives to break the monotony; although the girls never saw much of what happened beyond their station, they had heard about the braceros being fumigated; the girls also mention that their uncle had a store downtown, which was often frequented by braceros; later in the interview, Tula mentions Jesse Trevino.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.258 | 0.092 |
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".