Contaminated Sites and Indigenous Peoples in Canada and the United States: A Scoping Review
Bibliographic record
Abstract
Abstract Background Indigenous communities in Canada and the US are disproportionately exposed to contaminated sites, often arising from industrial and waste disposal activities. For instance, ∼34% of US EPA Superfund sites are of Native American interest, and ∼29% of Canadian federal contaminated sites are on Indigenous reserve land. Contaminated sites pose unique challenges to many Indigenous peoples who consider the land as an integral part of food systems, culture, and the economy. Federal management of contaminated sites is challenged by epistemological differences, regulatory barriers, and minimal scientific research. Objectives This scoping review aimed to identify and map information on contaminated sites and Indigenous peoples in Canada and the US, namely: 1) the relationship between contaminated sites and Indigenous people, and their land and food systems; 2) strategies, challenges, and successes for contaminated sites assessment and management on Indigenous land; and 3) Indigenous leadership and inclusion in contaminated site assessment and management. Methods Three streams of data were retrieved from January to March 2022: a systematic literature search (key word groups: Indigenous people and contaminated sites); a grey literature search; and an analysis of federal contaminated site data (Canada’s Federal Contaminated Sites Inventory (FCSI) and US EPA’s Superfund Database). Results Our search yielded 49 peer-reviewed articles, 20 pieces of grey literature, and 8114 federal site records (1236 Superfund, 6878 FCSI), evidencing the contamination of the lands of 815 distinct Indigenous tribes and nations and the presence of 440 different contaminants or contaminant groups. Minimal information is available on the potential health and ecological effects, assessment and management of risks, and collaboration on contaminated site processes relative to the number of sites on or adjacent to Indigenous lands. Discussion By integrating three diverse data streams we discovered a multi-disciplinary yet disparate body of information. The results point to a need to prioritize holism, efficiency, and Indigenous leadership in contaminated site assessment, management, and research. This should include a focus on community-specific approaches to site assessment and management; a re-conceptualization of risks related to sites that privileges Indigenous epistemologies; greater collaboration between networks such as the scientific community, Indigenous communities, and federal governments; and a re-evaluation of current management frameworks with Indigenous leadership at the forefront.
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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 | 0.008 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.032 | 0.045 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 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".