Trailblazers and Laggards: Explaining Variation in UNDRIP Implementation at the Subnational Level in Canada
Bibliographic record
Abstract
Abstract The 2007 adoption of the United Nations Declaration on the Rights of Indigenous Peoples (UNDRIP) marked a critical juncture in the area of Indigenous rights. As a nonbinding agreement, its adoption is at the discretion of each state, resulting in significant state-level variation. Importantly, within-state variations remain underexplored. These differences are potentially significant in federal, decentralized countries such as Canada. This article examines why some provinces and territories lead in implementing the key principles embedded in UNDRIP, whereas others have dragged their feet. We collected 230 Canadian regulations introduced at the subnational level between 2007 and 2023, and assessed the impact of three key variables (i.e. political ideology, resource politics and issue voting). We found that none of these variables explained within-state variations on their own. To further explore the role of these variables, we subsequently compared two provinces at different stages of the UNDRIP implementation spectrum (Québec and British Columbia).
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".