Bibliographic record
Abstract
The Pima Indians of Arizona offer one of the clearest examples of the connections between settler-colonial environmental engineering and health.The community suffers from one of the highest rates of diabetes in the world, with over half of the adult population diagnosed with the disease. 1 In 1877, the U.S. Congress passed the Desert Land Act, allowing settlers to claim arid or semiarid "public" lands in exchange for irrigating and cultivating them, thereby expressing in legislation a settler-colonial logic that views the frontier land as arid, in need of irrigation to produce crops and profit for settler communities.Subsequent "developmental" projects based on such reasoning, like the Roosevelt Dam (1903) and the Florence Diversion Dam (1922), have decreased the Pimas' water access by more than 60 percent, 2 caused irreversible damage to their food sovereignty and lifestyle, and introduced highly-processed market food with high sugar and fat content -a diet that has led to an epidemic of obesity and diabetes.Similar patterns can be seen among indigenous peoples across Canada, Australia, and New Zealand, where settlercolonial projects shattered the fabric of societies and destroyed indigenous farming, fishing, and food gathering practices, contributing to epidemics of obesity, hypertension, and heart diseases.3 Despite following a clearly similar pattern, Palestinian health is often excluded from discussions
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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 | 0.001 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| 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".