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Record W4412796219 · doi:10.1017/s000842392500023x

Trailblazers and Laggards: Explaining Variation in UNDRIP Implementation at the Subnational Level in Canada

2025· article· en· W4412796219 on OpenAlexafffundabout
Isabelle Côté, J. Andrew Grant, Matthew I. Mitchell, Dimitrios Panagos, Louis-Charles Vaillancourt

Bibliographic record

VenueCanadian Journal of Political Science · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicInternational Development and Aid
Canadian institutionsUniversity of SaskatchewanQueen's UniversityMemorial University of Newfoundland
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsVariation (astronomy)Political scienceRegional scienceGeography

Abstract

fetched live from OpenAlex

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).

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.878
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.028
GPT teacher head0.324
Teacher spread0.296 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2025
Admission routes3
Has abstractyes

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