Indigenous knowledge and federal environmental assessments in Canada: applying past lessons to the 2019 impact assessment act
Bibliographic record
Abstract
Policy-makers ideally pursue well-informed, socially just means to make environmental decisions. Indigenous peoples have used Indigenous knowledge (IK) to inform decisions about environmental management for millennia. In the last 50 years, many western societies have used environmental assessment (EA) processes to deliberate on industrial proposals, informed by scientific information. Recently EA processes have attempted to incorporate IK in some countries and regions, but practitioners and scholars have criticized the ability of EA to meaningfully engage IK. Here we consider these tensions in Canada, a country with economic focus on resource extraction and unresolved government-to-government relationships with Indigenous Nations. In 2019, the Canadian government passed the Impact Assessment Act, reinvigorating dialogue on the relationship between IK and EA. Addressing this opportunity, we examined obstacles between IK and EA via a systematic literature review, and qualitative analyses of publications and the Act itself. Our results and synthesis identify obstacles preventing the Act from meaningfully engaging IK, some of which are surmountable (e.g., failures to engage best practices, financial limitations), whereas others are substantial (e.g., knowledge incompatibilities, effects of colonization). Finally, we offer recommendations for practitioners and scholars towards ameliorating relationships between IK and EA towards improved decision-making and recognition of Indigenous rights.
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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 | 0.039 | 0.081 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.016 |
| Science and technology studies | 0.029 | 0.020 |
| Scholarly communication | 0.020 | 0.006 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.004 | 0.007 |
| 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".