Recommendation: Review of participation of Indigenous peoples in plastics pollution governance — R0/PR4
Bibliographic record
Abstract
While calls for Indigenous participation in plastics pollution governance are increasingly common, exactly what participation means remains unclear. This review investigates how English-language peer-reviewed and gray literature describe Indigenous participation and its barriers and analyzes the dominant terms, models, enactments, and theories of Indigenous participation in plastics pollution work. We find that different actors – Indigenous people and organizations, non-Indigenous authors, mixed collaborations, and settler governments and NGOs – are talking about participation in acutely different ways. Non-Indigenous actors tend to focus on the inclusion of Indigenous people, either as data, knowledge, or a presence in existing frameworks. Mixed Indigenous and non-Indigenous author groups focus on partnership and collaboration, though with significant diversity in terms of what modes of decision-making, rights, and leadership these collaborations entail. Indigenous authors and organization advocate for participation premised on Indigenous rights, sovereignty, creation, and leadership. We end by characterizing Indigenous Environmental Justice (IEJ) in the literature. IEJ provides a notably unique way of understanding and intervening in plastics pollution. The text is designed so researchers and organizers can be more specific, deliberate, and just in the way Indigenous peoples participate in plastic pollution research, initiatives, and governance.
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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 | 0.021 | 0.138 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.010 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.008 | 0.004 |
| Insufficient payload (model declined to judge) | 0.067 | 0.017 |
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".