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Record W4390951900 · doi:10.1017/s0008423923000719

Group-Based Affect and the Canadian Party System

2024· article· en· W4390951900 on OpenAlexaffabout
Sarah Lachance, Edana Beauvais

Bibliographic record

VenueCanadian Journal of Political Science · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicElectoral Systems and Political Participation
Canadian institutionsSimon Fraser UniversityUniversity of Toronto
Fundersnot available
KeywordsPluralism (philosophy)IdeologyAffect (linguistics)Political scienceFeelingPoliticsOutlierSocial psychologyPolarization (electrochemistry)Political economySociologyLawEpistemologyPsychologyComputer science

Abstract

fetched live from OpenAlex

Abstract In terms of party systems, Canada's system is an outlier. In our present work, we develop Richard Johnston's account of Canada's polarized pluralism in three ways. First, we link the literature on party systems to social identity theory. Second, we make an empirical contribution by directly testing Johnston's claim that intergroup affect plays a central role in shaping the dynamics of the party system. Using Canadian Election Study data from seven elections, we offer strong empirical support for the theory of polarized pluralism. Congruent with existing research, we find that the most important feature summarizing group-based affect in Canadian politics corresponds with the ideological left/right divide, but we also find that feelings toward groups on a second, uncorrelated axis (feelings toward Quebec and minority groups) shape vote choice. Yet our results show that fault lines in the polarized pluralist structure of the Canadian party system are emerging.

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 score0.048
Threshold uncertainty score0.148

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0050.002
Scholarly communication0.0020.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0070.000

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 head0.032
GPT teacher head0.328
Teacher spread0.295 · 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

Citations3
Published2024
Admission routes2
Has abstractyes

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