Bibliographic record
Abstract
Joel R. Hyer investigates health conditions on one Indian reservation in Southern California during the first two decades of the twentieth century. Hyer contends that unsanitary conditions on the Pala Reservation actually facilitated the spread of diseases among local Cupeño, Luiseño, and Kumeyaay Indians. He also describes how the United States federal government employed doctors, field matrons, and others to promote good health and combat disease among Indians at Pala. The author asserts that, despite their altruistic intentions, some of these government workers attempted to discourage local indigenous peoples from consulting their shamans for medical attention - Native healers whose extensive knowledge of roots and herbs had cured many forms of illness for years. In addition, the teacher at the reservation's day school sought to prevent the spread of disease among her Indian pupils by exposing them to American modes of health care. Furthermore, Hyer maintains that one national health program, the "Save the Babies" campaign, was successful on the Pala Reservation because Indian mothers believed in it and followed specific guidelines to lower mortality rates among their own children. Throughout his essay, the author addresses the pressures of assimilation and acculturation that were so accute at this time in the United States. Hyer concludes by suggesting that many of his findings reflect broader health trends on Indian reservations throughout the United States during this period.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.023 | 0.012 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".