Indigenous guardians as an emerging approach to indigenous environmental governance
Bibliographic record
Abstract
Over the past 3 decades, indigenous guardian programs (also known as indigenous rangers or watchmen) have emerged as an institution for indigenous governments to engage in collaborative environmental governance. Using a systematic review of peer-reviewed literature for research conducted in Australia, Canada, Aotearoa-New Zealand, and the United States, we sought to characterize the emergence of indigenous guardians in the literature and explore whether guardian approaches are representative of Indigenous approaches to environmental governance. Using a multistep relevance-screening method, we reviewed 83 articles published since 1995, that report on, critique, or comment on Indigenous guardians. Our findings indicated that most articles on the topic were published in the last decade (88%), focused on Australia (65%), and were in a social science discipline (53%). The lead author of the majority of articles was an academic, although only half of the articles included an indigenous scholar or member of an indigenous group or organization as a coauthor. Finally, 11 articles were on research of guardian programs that were locally led and only 5 exemplified indigenous governance, based on 2 well-known community-based monitoring typologies. Our findings indicate that more research is required to understand the implications of current guardian programs for indigenous self-determination, particularly when such programs are embedded in a broader western environmental governance structure.
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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 | 0.013 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".