The Same River, Together: Theorizing and Assessing Shared Rule in Treaty Federalism
Bibliographic record
Abstract
Starting from a query regarding the place of pre-Confederation treaties in the reconciliatory intentions of the contemporary Canadian political order, the primary objective of my dissertation is to consider one possible answer to this challenge by asking whether and how Indigenous peoples’ aspirations for both self-rule and shared rule might be achieved, and what role – if any – the Canadian federal state might play in those aspirations. The theory of treaty federalism, which takes the pre-Confederation treaties as the foundation of a federal relationship between Indigenous peoples and Canada’s colonial authorities, presents one vision for achieving such aspirations. The general aim of this dissertation is to consider both the theoretical basis for a theory of shared rule in treaty federalism as well as its implications for assessing the merit of various institutional models operationalizing shared rule. However, the theory of treaty federalism has been criticized for its under-theorization of the element of shared rule. This dissertation fills the shared rule gap in the theory of treaty federalism by elaborating what I have termed a “sharing protocol” that could feasibly underpin treaty relations in the Canadian federal order. If a functioning, treaty-centered nation-to-nation relationship is ever to be within reach, a coherent and comprehensive theory of shared rule is imperative. The sharing protocol rests on three core principles, derived from historical and contemporary treaty processes and traditions: enacting self-determination, commitment to co-responsibility and reciprocal prosperity. To test the viability of these principles in operationalizing a treaty federal order, the dissertation assesses the core models of political representation employed in service of increasing Indigenous political presence in settler state legislative bodies around the globe. Ultimately, the dissertation finds that both operational and proposed models of Indigenous political representation offer only limited enactments of shared rule from the perspective of a theory of treaty federalism, most often constrained by a lack of affirmation for Indigenous self-determination and the imposition of state-centric and hierarchical, rather than nation-to-nation, relations.
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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 | 0.022 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.014 | 0.062 |
| Scholarly communication | 0.017 | 0.024 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.004 | 0.006 |
| 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".