Evaluation of criteria for meaningful Aboriginal consultation in Canada during environmental assessment
Bibliographic record
Abstract
Developments on traditional Aboriginal territory in Canada often require environmental assessments (EAs) to predict potential environmental and social impacts. Impacts considered during the Canadian government’s (Crown) consultation process (between the Crown, Aboriginals and proponents), is known as the Duty to Consult (DtC). The DtC is a legal requirement under the Constitution Act, 1982 and case law, which provides opportunities for Aboriginal rights and interests to be protected by identifying and mitigating impacts. Scope of consultation depends on the strength of Aboriginal claim and level of adverse impacts. However, in Canada, there is a lack of DtC guidelines. Whilst a lack of guidelines offers flexibility, it also presents many implementation challenges. Current DtC practices often result in proponent withdrawal, delays, protests, conflict and creates risks to project development. This paper assesses application of DtC criteria (established in EA literature), and compares them against three recent case studies across Canada. Although results show some inadequate practices, there are examples of effective DtC criteria which help reduce impacts to Aboriginal rights and land. Recommendations to improve the DtC process that benefit all stakeholders includes: increased Crown guidance to proponents; acknowledging benefits of Free, Prior, Informed Consent; increased utilization of measures outlined in EA legislation including extensions and suspensions; and stronger, earlier consultation legislation in federal and provincial EA guidelines. The above recommendations seek to reveal that more effective consultations are those which prioritize relationship building, consensus seeking and are responsive to each First Nations circumstances. Although qualitative, this study provides evidence based criteria for effective Aboriginal consultation approaches within the Canadian EA process.
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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 | 0.108 | 0.254 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.022 | 0.007 |
| Scholarly communication | 0.014 | 0.004 |
| Open science | 0.005 | 0.008 |
| 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".