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Record W1896158350 · doi:10.7202/1032504ar

Vote stratégique au Québec : analyse de l’élection de 2012

2015· article· fr· W1896158350 on OpenAlexaffvenueabout
Jean‐François Daoust

Bibliographic record

VenuePolitique et Sociétés · 2015
Typearticle
Languagefr
FieldSocial Sciences
TopicElectoral Systems and Political Participation
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsPolitical scienceHumanitiesPhilosophy

Abstract

fetched live from OpenAlex

Bien que le vote stratégique soit un thème récurrent lors des campagnes provinciales électorales québécoises, aucune étude n’a analysé le vote stratégique au Québec. En utilisant un sondage du projet Making Electoral Democracy Work, cet article comble cette lacune dans la littérature et analyse l’élection québécoise de 2012 en répondant à deux objectifs principaux : déterminer la proportion de votes stratégiques et identifier les variables individuelles qui influencent la probabilité qu’un électeur opte pour une coordination stratégique. Les résultats indiquent que, dans l’ensemble, 8,4 % des votes peuvent être dits stratégiques. Quant aux déterminants du vote stratégique et tel qu’attendu, le fait pour un électeur d’être partisan et d’avoir un plus grand écart de préférence entre les deux options favorites diminue sa propension à déserter son premier choix. Toutefois, contrairement aux attentes, le niveau de sophistication politique n’influence pas significativement la probabilité qu’un électeur opte pour une coordination stratégique.

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)0.001
metaresearch head score (Gemma)0.004
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 score0.021
Threshold uncertainty score0.155

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.005
Science and technology studies0.0020.000
Scholarly communication0.0020.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0100.001

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 head0.172
GPT teacher head0.488
Teacher spread0.316 · 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

Citations12
Published2015
Admission routes3
Has abstractyes

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