Native American and First-Nations Canadian and Physical PFAS Accumulations: A Literature Review
Bibliographic record
Abstract
Native communities' exposure to anthropogenic Per- and Polyfluoroalkyl Substances (PFAS: "Forever Chemicals") are not demographically or geographically evenly distributed. Those who live near or on contaminated land or water are the most likely to accumulate dangerous amounts of PFAS that may have serious health consequences (Tribal PFAS Working Group 2021). Areas that tend to be most contaminated include landfills, airports, and military bases. Often Black, Indigenous People of Color (BIPOC) and low-income communities are placed near these "sacrificial zones" due to historic and current policies that segregate and marginalize people and families to polluted lands creating great environmental injustices (Dixie). In this thesis I will discuss 14 articles that delve into PFAS bodily accumulations in Native communities North of the 60th meridian in the U.S. and Canada and will come to understand that food and general practices deeply effect PFAS accumulation from community to community. Though there is overlap in some of the data found in this review, generalized understandings about Native peoples' bodily accumulations of PFAS in the region are hard to determine, and should be approached cautiously. I therefore conclude that to truly understand PFAS in Native communities, first-foods, first-medicines, and drinking water need to be tested. Native food, medicine, and water sovereignty should be centered, and Native leadership is paramount.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.014 | 0.024 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".