Bibliographic record
Abstract
In 2015, Canada saw a Liberal government form under the young and energetic leadership of Justin Trudeau. After a Conservative government under Stephen Harper, Trudeau set out to bring a ‘fresh and exciting’ vision of Canada that prioritized reconciliation with Indigenous peoples. Thus “reconciliatory federalism” was born. Since then, discussions between Indigenous leaders and the federal government have increased exponentially, yet the undertones of Canada’s colonial history still play an evidentiary role in Canadian federalism. This paper seeks to evaluate Trudeau’s “reconciliatory federalism” in relation to the scholarly literature pertaining to Indigenous self-determination and Canadian federalism. Moreover, using definitions of Kiera Ladner’s treaty federalism and Martin Papillon’s multi-level governance as a theoretical framework, I seek to investigate if Trudeau’s vision of reconciliatory federalism can bridge the two scholarly camps together. While treaty federalism argues for a top-down approach to establish a nation-to-nation relationship between Indigenous peoples and the state, multi-level governance argues for a bottom-up approach in which Indigenous peoples find multiple avenues within the existing federalist structure to integrate into. Using the examples of the Wet’sewet’en Cree First Nation and the Manitoba Métis Federation, I seek to contextualize the implications of reconciliatory federalism in relation to the two scholarly camps.
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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 | 0.009 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.022 | 0.039 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".