Tools for Indigenous-led impact assessment: insights from five case studies
Bibliographic record
Abstract
Indigenous-led impact assessment (ILIA) is a project review process designed and conducted with meaningful input and an adequate degree of control by Indigenous peoples. Using a case-based approach, this paper examines ILIAs conducted in Canada. The research – tools for ILIA – provides examples of options for the design and implementation of ILIA processes which have been utilized by Indigenous Nations while making their own determinations regarding if and how development should occur according to their unique locations, histories, natural resource issues, and governance. We have identified five tools: framework agreements; customized review panels; land use and consultation policy; impact and benefit agreements; and land use planning. Each tool is described along with a case study example of how the tool was applied within ILIA. Although our work focuses on Canada, the examples and tools can be valuable for Indigenous peoples and EIA practitioners in jurisdictions elsewhere who are looking to understand how ILIA might be operationalized to reflect their settings, values, and priorities. The results are helpful to Indigenous governments and groups looking to develop their own approaches to assessment, and for understanding the relative strengths and experiences of options they may consider or adapt for their own needs.
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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 | 0.070 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.014 | 0.007 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| 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".