Cross-Canada Infrastructure Corridor, the Rights of Indigenous Peoples and 'Meaningful Consultation'
Bibliographic record
Abstract
Pursuit of a cross-country infrastructure corridor raises complex legal issues with respect to the rights and interests of Indigenous peoples in Canada. The legal context has changed significantly since the corridor concept was initially presented in the 1960s. This article sets out the diverse legal landscape across treaty and non-treaty contexts in Canada today, and then describes Crown obligations with respect to Indigenous peoples, including “meaningful consultation”, that would be involved in pursuing the corridor concept. A key observation is that, while the jurisprudence provides relatively comprehensive guidance on the meaningful consultation standard, the contextual nature of the duty to consult legal framework will make it difficult to achieve in the practical Corridor context. This article also puts forward preliminary comments and queries with respect to legal forms that the Corridor concept make take and formal forums in which Crown consultation might occur, including in relation to the new federal impact assessment regime. Overall, this article observes that tensions, complexities and sensitivities that have produced friction in the contemporary legal sphere pertaining to large linear infrastructure projects and the rights of Indigenous peoples would still be present in pursuing the corridor proposal. Meanwhile, further change in the law is entirely foreseeable.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.024 | 0.029 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".