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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.004 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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; both teacher heads agree on what is shown here.
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".