Tuberculosis, Blackfeet, and the State
Bibliographic record
Abstract
Tuberculosis dates to the ancient world and persists as one of the deadliest contagions today. The World Health Organization estimates that as of 2020, tuberculosis infects one quarter of the global population. From the late-nineteenth through the mid-twentieth centuries, tuberculosis ravaged Montana Native American populations due in large part to the reorganization of tribal lands coupled with mass migrations of non-Native peoples. With the establishment of reservations leading to closer living quarters and further federal Indian policies such as land allotment, Native infection rates increased significantly with deaths greatly exceeding other population groups. During the early twentieth century, Progressive health reformers honed in on tuberculosis treatments, but these policies did not tend to convert to Indian healthcare. Though tuberculosis cases were on the rise, especially after the Influenza pandemic of 1918-19, funding for the Blackfeet Tuberculosis Sanitarium ceased in 1922. This closure contrasts with the push for national Progressive health reform. Few scholars have focused on tuberculosis within Montana reservations other than sideline topics or statistics. This presentation explores the Public Health power dynamics between Blackfeet Indians, the State of Montana, and the Bureau of Indian Affairs during the early twentieth century. BIA agents and land allotment surveyors in Montana wrote extensively about the increase of tuberculosis cases while pleading for further aid to combat the contagion. In 1932, a 5-year study published by the Montana State Board of Health concluded that tuberculosis accounted for 34% of deaths for Native populations in comparison to 4% of non-Natives. Statistics such as these led to an increased concern for Indian healthcare. Examination of BIA, State, Senatorial, field matron, sanitarium, and Montana tuberculosis association records will illuminate why the defunding occurred and if there were alternate healthcare options or if they were left to flounder without support. It will additionally determine if healthcare was compulsory or voluntary. Further exploration of infection rates at boarding and day schools will determine healthcare for students. By reading against the grain and pairing these colonial sources with oral histories, family papers, and photographs, Blackfeet individuals move from the periphery of the historical narrative. This will elucidate the Native fight for body sovereignty by attempting to navigate or avoid Progressive healthcare policies and governance.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".