Process and Reconciliation: Integrating the Duty to Consult with Environmental Assessment
Bibliographic record
Abstract
As the duty to consult Aboriginal peoples becomes operationalized within the frameworks of government decision-making, the agencies responsible for these decisions are increasingly turning to environmental assessment (EA) processes as one of the principal vehicles for carrying out those consultations. This article explores the practical and theoretical dimensions of using EA processes to implement the duties to consult and accommodate. The relationship between EA and the duty to consult has arisen in a number of cases and a clear picture is emerging of the steps that agencies conducting EAs must carry out in order to discharge their constitutional obligations to Aboriginal peoples. The paper examines the implementation of the duty to consult through various stages of the EA processes, identifying the EA practices that are best able to satisfy the legal requirements and the aspirations of the duty to consult, as well as to identify areas that are likely to present challenges moving forward. The paper also considers the broader approach to EA that is more likely to contribute to the overarching goal of reconciliation. Here the central claim is that greater attention must be paid to the deliberative and justificatory qualities of EA.
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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 | 0.073 | 0.074 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.018 | 0.051 |
| Scholarly communication | 0.019 | 0.021 |
| Open science | 0.004 | 0.024 |
| Research integrity | 0.018 | 0.013 |
| Insufficient payload (model declined to judge) | 0.004 | 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".