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.”
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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; both teacher heads agree on what is shown here.
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".