Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.008 |
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 it