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Record W4390907218 · doi:10.18254/s207054760029527-6

2022 Provincial elections in Quebec: consolidation or fragmentation?

2023· article· en· W4390907218 on OpenAlexaboutno aff
Yury Akimov

Bibliographic record

VenueRussia and America in the 21st Century · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicPolitical Systems and Governance
Canadian institutionsnot available
Fundersnot available
KeywordsAppealPoliticsPolitical scienceNationalismPolitical economyConsolidation (business)PopularityElectoral systemVotingFederal electionGeneral electionPolarization (electrochemistry)Public administrationLawSociologyDemocracyEconomics

Abstract

fetched live from OpenAlex

The article deals with the election campaign and the results of the general elections to the National Assembly of Quebec held on October 3, 2022. The author claims that these elections demonstrated the preservation of fairly broad support for the Coalition avenir Québec (CAQ), a sharp drop in the popularity of the sovereignist project championed by the Parti québécois (PQ), a clear split in electoral preferences between “Big Montreal” consistently voting for the provincial Liberals (PLQ) and “The Rest of Québec”, as well as a certain increase in the polarization of the provincial electorate and its protest moods. It is noted that during these elections, the shortcomings of the existing electoral system became particularly evident. It is stressed that québécois nationalism, to which all the leading political forces of Québec appeal, remains the most important driving force of provincial politics. The author points out that the modern Québec politics is characterized by increased attention to issues of federal-provincial relations, protection of the French language, French-Québec culture and identity, with a clear decline in separatist sentiments and against the background of the expansion of the political spectrum.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.839
Threshold uncertainty score0.912

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.014
GPT teacher head0.301
Teacher spread0.287 · 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 designTheoretical or conceptual
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

Citations1
Published2023
Admission routes1
Has abstractyes

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