Operationalizing Indigenous-led Impact Assessment
Bibliographic record
Abstract
Recent years have ushered in an explosion of interest and expertise in place-based, Indigenous-led impact assessment models. Across Canada and beyond, Indigenous communities have been developing and engaging with alternative approaches to “environmental assessment” (EA) or “impact assessment” (IA) in response to proposed developments in their homelands. These efforts are borne out of deep dissatisfaction and frustration; Indigenous peoples have repeatedly pointed to the inability of settler law on EA to protect their constitutionally recognized Aboriginal and Treaty rights, and to meaningfully engage with Indigenous laws, values, and perspectives regarding the socio-ecological risks posed by resource development projects. The inability of EA under settler law to adequately consider Indigenous legal orders and jurisdictions has been well documented. As Coast Salish legal scholar Sarah Morales notes, “[m]ost Canadian Indigenous groups have not had a meaningful voice in impact assessment,” and “rarely has any Indigenous group been able to exercise consent or decision making on major resource development projects.” More often, when Indigenous groups participate in government regulatory processes, “other parties severely limit their involvement, requesting only baseline traditional knowledge and traditional use information, without any meaningful input into or control over the process or project itself.” The result is that “Indigenous culture, spirituality, laws and legal processes, rights and title have not been taken into account in the Crown-led and proponent-driven Canadian environmental assessment processes.”
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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 | 0.026 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.005 | 0.014 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".