Key Principles for Indigenous and non-Indigenous Collaborative Impact Assessment in Canada
Bibliographic record
Abstract
Impact assessment (IA) is an influential planning tool used to evaluate the potential effects, benefits, and risks of project-level resource development. Indigenous peoples are often disproportionately affected by the adverse consequences of resource exploitation. However, the processes employed in Canadian IA to engage with Indigenous people have faced criticism, particularly on the following five matters: scope and coverage of impacts are inadequate; funding is insufficient; Indigenous knowledge is largely ignored; Indigenous communities do not set the terms of IAs; and Indigenous consent is not required as a condition of approval for projects that will affect Indigenous people or territories. Recently, changes in law and policy have given rise to a growing literature on collaborative IA (where the assessment is conducted by a non-Indigenous authority in partnership with Indigenous authorities) in Canada. This research employs an integrative literature review and a case study analysis to identify and evaluate the most commonly stated foundations of collaborative IA, and therefore the apparent underlying basis of the broad Canadian experience to respect and empower (without integrating) both Indigenous and non-Indigenous objectives, perspectives, and distinct ways of knowing in collaborative IA.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.025 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.029 | 0.036 |
| Scholarly communication | 0.021 | 0.005 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 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".