Including Indigenous Knowledge Systems in Environmental Assessments: Restructuring the Process
Bibliographic record
Abstract
Indigenous peoples around the world are concerned about the long-term impacts of industrial activities and natural resource extraction projects on their traditional territories. Environmental impact studies, environmental risk assessments (EAs), and risk management protocols are offered as tools that can address some of these concerns. However, these tools are not universally required in jurisdictions, and this Forum intervention considers whether these technical tools might be reshaped to integrate Indigenous communities’ interests, with specific attention to traditional knowledge. Challenges include unrealistic timelines to evaluate proposed projects, community capacity, inadequate understanding of Indigenous communities, and ineffective communicatio, all of which contribute to pervasive distrust in EAs by many Indigenous communities. Despite efforts to address these problems, substantive inequities persist in the way that EAs are conducted as infringement continues on constitutionally protected Indigenous rights. This article highlights challenges within the EA process and presents pathways for improving collaboration and outcomes with Indigenous communities.
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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 | 0.168 | 0.116 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.021 | 0.034 |
| Scholarly communication | 0.025 | 0.032 |
| Open science | 0.005 | 0.037 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".