Bibliographic record
Abstract
Mr. Saucedo talks about his family and childhood; he initially enlisted in the bracero program in 1945, in Ciudad Juárez, Chihuahua, México, but he was then transferred to Querétaro, Querétaro, México, in order to complete the paperwork; from there, he was transported by train back to Juárez and into the United States before finally being taken to Nevada, where he worked on the railroads; in addition, he describes the various procedures he underwent while being processed; as a bracero, he went to work picking different crops in several places throughout the United States, including Arizona, Arkansas, Kansas, Nebraska, New Mexico, and Texas; he goes on to discuss his duties, wages, working and living conditions, provisions, hardships, recreational activities, religion, and relationships with fellow employees and employers; in 1955, he legalized his residency, and continued working on a walnut farm in La Mesa, New Mexico; eventually, he was able to legalize residency for his family, and they all moved to the United States; he concludes by describing his fondness for the United States, and he reflects happily on his bracero experiences.
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 | 0.006 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.185 | 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".