Bibliographic record
Abstract
Ms. Norquest recalls growing up as a child on her father’s farm; her family owned 100 acres of land, and they rented another 200 acres; their primary crop was cotton, but they also had carrots, citrus, corn, grain, and tomatoes; she and her siblings would help during the harvest by picking and weighing cotton; in the late 1940s and all through the 1950s, her father hired braceros to help with the crops; there was an average of five to ten workers that stayed on year round, and more during the harvesting season; her father hired a number of skilled laborers, such as irrigators and tractor drivers, on a permanent basis, and a few of them later became United States citizens; she mentions that her father had to abide by strict government standards with regard to housing, pay, and medical insurance; some of the braceros preferred going to doctors in Mexico, and her father would drive them across the border if necessary; he would also give workers bonuses at the end of a season as an incentive for them to come back and work for him; she recalls one instance when her father did not have enough money to pay everyone the minimum wage, but the they agreed to work for him anyway; one worker reported him to government officials, but he was shunned by the bracero community for having made such a statement; she goes on to recall other specific incidents with braceros as well; overall, her family developed great relationships with the braceros, and a number of them stayed in touch with the family long after they stopped working together.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.278 | 0.088 |
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".