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Record W4383878375 · doi:10.1080/13597566.2023.2233422

Is Quebec independence still key in making sense of Canadian elections? A longitudinal analysis (2000–2021)

2023· article· en· W4383878375 on OpenAlexaffabout
Jean‐François Daoust, Thomas Gareau‐Paquette

Bibliographic record

VenueRegional & Federal Studies · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicPolitical Systems and Governance
Canadian institutionsMcGill UniversityUniversité de Sherbrooke
Fundersnot available
KeywordsIndependence (probability theory)Political scienceKey (lock)Public administrationComputer scienceStatisticsComputer securityMathematics

Abstract

fetched live from OpenAlex

The issue of Quebec independence was, until recently, considered the most important factor motivating Quebeckers’ vote choice in federal elections. However, the conventional wisdom has shifted and now holds, especially since 2011, that the explanatory power of the issue of Quebec secession would have decreased over time and would no longer be deemed as one of the few decisive determinants of citizens’ vote choice calculus. We provide the first longitudinal analysis of the impact of Quebec independence in federal elections using the Canadian Election Study from 2000 to 2021. Our findings show that, as expected, attitudes toward Quebec secession in explaining citizens’ vote choice were more important at the beginning of the twenty-first century, although only when comparing the discrepancy between the 2000 and 2004 elections. Between 2006 and 2021, the importance of Quebec independence has remained stable and key in explaining electoral outcomes.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.031
Threshold uncertainty score0.223

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.003
Science and technology studies0.0040.001
Scholarly communication0.0030.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.102
GPT teacher head0.369
Teacher spread0.267 · 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 source (direct Gemma or distilled Codex), 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

Citations9
Published2023
Admission routes2
Has abstractyes

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