Inuit, Tuberculosis, and Political Determinants of Health
Bibliographic record
Abstract
Tuberculosis is one of humanity's most ancient and deadly diseases. It is largely curable, but its long co-evolution with humans has given it distinctive characteristics that make it hard to control or eradicate. The persistence of tuberculosis is usually attributed to social determinants of health. Yet, history shows that political determinants are more fundamental to its epidemiology. While tuberculosis is indeed shaped by social factors, along with biomedical and sometimes geographic factors, its distinctiveness makes it an especially expensive disease. This in turn makes it political, as decisions are made on how or even whether to allocate resources to treat it. Political dynamics are clearly seen in the history of tuberculosis among Inuit in Canada. Inuit bear a burden of disease among the highest in the world. The burden has lasted for more than a century, but it has not been uniform. Political factors shaped the history into four periods, each with a distinctive manifestation of tuberculosis. The most clear illustration of underlying forces comes in the most anomalous period, starting in the late 1960s, which centred on a unique project in Frobisher Bay. Inuit were given leading-edge treatments locally, and disease rates dropped dramatically. Yet the project was quickly cancelled. The factors behind the project and its cancellation are examined through a cross-disciplinary approach, drawing on archival records, social science and scientific writing, and recent genomic studies. These demonstrate that political determinants of health are the "determinants of determinants" of tuberculosis.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.009 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".