Dispute Resolution in Modern Treaties: Evolutions, Observations and Next Steps
Bibliographic record
Abstract
In Canada, comprehensive land claims agreements – often called modern treaties – between the government and Indigenous nations include provisions prescribing how disputes between treaty parties are to be resolved. Experiences with these dispute resolution mechanisms vary across treaty contexts and there is substantial variance in the terms of these treaties. To date, this dimension of modern treaty implementation has received minimal scholarly attention, despite calls for such research. Drawing on specific examples, this article sets a foundation for further research by examining the significant variation across different treaties’ dispute resolution mechanisms and commenting on key differences, similarities and other notable features. A key focus of the analysis is on the observable evolution of these mechanisms from a relatively narrow arbitration board model to a more flexible “staged approach”. The analysis suggests that the latter may provide a stronger basis for joint problem-solving and integrative bargaining, notwithstanding open questions about the extent to which such approaches are warranted in fraught Crown-Indigenous relationships in Canada. The article also discusses the conspicuous absence of dispute resolution mechanisms that accommodate, let alone require, approaches rooted in the traditional or cultural practices of Indigenous treaty parties. Observations throughout are contextualized in relation to a growing body of jurisprudence and a broader context of fast-changing federal law and policy in Canada, which may set the stage for amendments to the dispute resolution provisions of modern treaties.
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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 | 0.023 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.011 |
| Science and technology studies | 0.012 | 0.035 |
| Scholarly communication | 0.015 | 0.010 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 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".