Impact assessment and indigenous self-determination: a scalar framework of participation options
Bibliographic record
Abstract
The implementation of the right of indigenous peoples to participate in impact assessment (IA) has moved rapidly in many jurisdictions. To facilitate comparative learning, this paper offers a scalar framework of participation options through standard IA phases and examines five IA regimes in Sweden, Norway, Canada, Australia, and Aotearoa/New Zealand. It is shown how practice is moving toward co-management and community-owned IA, with developments driven by strong indigenous demands and political recognition of material rights to lands and resources. Yet, while influence in IA has allowed for shaping project outcomes it has rarely supported the rejection of unwanted projects altogether. Moreover, some jurisdictions, such as Scandinavia, retain a much more limited consultation and notification approach. Community influence tends to be in evidence generation and follow-up while developers or state authorities retain control over decisive phases of scoping and significance determination. It is argued that indigenous participation is most meaningful through IA co-management that takes places directly with the state and throughout all IA phases, complemented with strategic community-owned IA.
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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 | 0.034 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.005 | 0.054 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".