A State-of-the-Art Review of Indigenous Peoples and Environmental Pollution
Bibliographic record
Abstract
Abstract Indigenous peoples (IPs) worldwide are confronted by the increasing threat of pollution. Based on a comprehensive review of the literature (n = 686 studies), we present the current state of knowledge on: 1) the exposure and vulnerability of IPs to pollution; 2) the environmental, health, and cultural impacts of pollution upon IPs; and 3) IPs' contributions to prevent, control, limit, and abate pollution from local to global scales. Indigenous peoples experience large burdens of environmental pollution linked to the expansion of commodity frontiers and industrial development, including agricultural, mining, and extractive industries, as well as urban growth, waste dumping, and infrastructure and energy development. Nevertheless, IPs are contributing to limit pollution in different ways, including through environmental monitoring and global policy advocacy, as well as through local resistance toward polluting activities. This work adds to growing evidence of the breadth and depth of environmental injustices faced by IPs worldwide, and we conclude by highlighting the need to increase IPs' engagement in environmental decision-making regarding pollution control. Integr Environ Assess Manag 2020;16:324–341. © 2019 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals, Inc. on behalf of Society of Environmental Toxicology & Chemistry (SETAC) KEY POINTS This review maps out the worldwide impacts of environmental pollution on Indigenous Peoples. Indigenous Peoples experience large burdens of environmental pollution linked to the expansion of commodity frontiers and industrial development. Indigenous Peoples are amongst the populations at highest risk of impact by pollution of water, land and biota through both exposure and vulnerability. Indigenous Peoples actively contribute to develop innovative strategies to limit, prevent and abate pollution from local to global levels
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".