Research Article: Collaborative Environmental Governance and Indigenous Peoples: Recommendations for Practice
Bibliographic record
Abstract
Collaborative environmental governance scholars have increasingly recognized the need to engage Indigenous peoples in environmental decision-making processes. Barriers to doing so effectively are well known. Recognizing these barriers, some scholars have discussed recommendations from practice in regions where Indigenous lands have been colonized and where there are complex environmental problems. This article explores assumptions regarding Indigenous engagement in the practice of collaborative environmental governance and contextualizes these assumptions relative to the perspectives of Indigenous peoples. Concrete advice for environmental practitioners is offered that builds on findings from a previously published systematic review and empirical multi-case study of governance for water in British Columbia, Canada. Recommendations for practice offered here include the following: approach or involve Indigenous peoples as self-determining nations rather than as one of many collaborative stakeholders or participants; identify and engage with existing or intended environmental governance processes and assertions of self-determination by Indigenous nations; create opportunities for relationship building between Indigenous peoples and policy or governance practitioners; choose venues and processes of decision making that reflect Indigenous rather than Eurocentric venues and processes; provide resources to Indigenous nations to level the playing field in terms of capacity for collaboration or for policy reform decision making; and find ways to support Indigenous nations in their own continued environmental decision making and self-determination.
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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 | 0.068 | 0.108 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.009 | 0.011 |
| Scholarly communication | 0.015 | 0.026 |
| Open science | 0.006 | 0.014 |
| Research integrity | 0.013 | 0.009 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".