The Tin Ear of the Court: Ktunaxa Nation and the Foundations of the Duty to Consult
Bibliographic record
Abstract
The recent Ktunaxa Nation decision of the Supreme Court of Canada provides an opportunity to discuss the fundamental legal presumptions that underlie the Crown’s duty to consult and accommodate Aboriginal peoples. The jurisprudence in this area has been based on a “thick” conception of Crown sovereignty as including legislative power and underlying title in relation to Aboriginal lands. This, in the Supreme Court’s view, justifies the possibility of the unilateral infringement of Aboriginal rights. This framework assumes that the relationship between the Crown and Aboriginal peoples is a sovereign-to-subjects one. This assumption, however, lacks a legal and factual basis. Conversely, Aboriginal peoples articulate their claims in the language of inherent jurisdiction within a nation-to-nation relationship. If the Supreme Court acknowledged that the relationship between the parties is indeed nation-to-nation, the appropriate doctrine would no longer be a duty to consult and accommodate. Following the approach to a similar relationship outlined by the Supreme Court in the Secession Reference, the appropriate model would be a generative duty to negotiate. This article sets a path to a model that preserves the useful components of the duty to consult while providing a remedy to the distributional inequity in bargaining power created under the current framework, thereby opening avenues for effective conflict resolution.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.027 | 0.023 |
| Scholarly communication | 0.015 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.010 | 0.011 |
| 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".