A review of Indigenous knowledge and participation in environmental monitoring
Bibliographic record
Abstract
There is a growing interest by governments and academics in including Indigenous knowledge alongside scientific knowledge in environmental management, including monitoring. Given this growing interest, a critical review of how Indigenous peoples have been engaged in monitoring is needed. We reviewed and analyzed the academic literature to answer the following questions: How have Indigenous peoples participated in environmental monitoring, and how has their participation influenced monitoring objectives, indicators, methods, and monitoring outcomes? We also summarized how this literature discussed power, governance, and the use of both Indigenous and scientific knowledge in environmental monitoring efforts. We found that the literature most often characterized participation as data collection, and that higher degrees of participation and power held by Indigenous peoples in environmental monitoring leads to initiatives that have different objectives, indicators, and outcomes than those with heavier involvement of external groups. Our review also showed that a key challenge of conducting effective monitoring that leverages both Indigenous knowledge systems and science is the power imbalances that uncouple Indigenous monitoring efforts from management. We encourage future initiatives to carefully consider the ways in which power and governance shape their programs and the ability of their monitoring to lead to meaningful management actions.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.009 |
| 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) | 0.003 | 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".