Aboriginal Rights: Creating Disincentives to Negotiate: Mitchell v. M.N.R.'S Potential Effect on Dispute Resolution
Bibliographic record
Abstract
The Supreme Court of Canada has often encouraged the Crown and Aboriginal parties to find negotiated solutions to their disputes. The complex social, political, economic and legal interests which are embedded in many sectors of the Canadian population are not best resolved in the context of legal proceedings. Courts should, however, do more than lament the lack of negotiations - they should make decisions that create incentives for high quality, effective dispute resolution processes. This article describes the framework for negotiation set out in R. v. Sparrow (on Aboriginal rights to fish), Delgamuukw v. British Columbia (on Aboriginal title to lands) and Marshall v. Canada (on treaty rights to fish). Those cases would provide incentives for the parties to negotiate. By contrast, in the case of Mitchell v. M.N.R. (exemption from duty on border crossing), the two judgments of the Supreme Court turn on the interpretation of history and the incompatibility with Canadian sovereignty. While it is not inappropriate to take those factors into account, the Court sets those up as threshold issues that need to be resolved before the Court would consider how to balance Aboriginal rights with Crown infringements. Unfortunately, the approach used in Mitchell will provide disincentives to negotiate workable accommodations for contemporary problems.
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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 | 0.005 | 0.014 |
| 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 | 0.014 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 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".