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Record W3180582544 · doi:10.1093/pa/gsab003

Guns for Votes: Wedge Politics in the Canadian Multiparty System

2021· article· en· W3180582544 on OpenAlex
David Dumouchel, Catherine Ouellet, Thierry Giasson

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueParliamentary Affairs · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicElectoral Systems and Political Participation
Canadian institutionsUniversité LavalUniversity of TorontoUniversité de Montréal
Fundersnot available
KeywordsOpposition (politics)Electoral systemPoliticsPolitical sciencePublic administrationPolitical economyWedge (geometry)Empirical evidenceLawDemocracySociology

Abstract

fetched live from OpenAlex

Abstract This article contributes to the emerging literature on wedge issues and on electoral strategies in multiparty electoral systems by studying the implementation and effectiveness of a concerted electoral communication strategy deployed by the Conservative party of Canada around the elimination of the gun registry, in the late 2000s. First, using a quantitative content analysis of the Parliamentary debates from 2006 to 2011, it reveals how the Conservatives exploited the issue in order to create divisions amongst opposing opposition MPs and amongst targeted segments of partisan voters of other national parties. Secondly, using georeferenced pool data from Vote Compass, it finds empirical evidence that the Conservatives’ efforts won them new support in the 2011 federal elections, especially amongst cross-pressured voters and within the ridings that were targeted during the debates. In doing so, the article provides a rare empirical example of wedge politics carried in a multiparty system.

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.

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)0.000
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 score0.870
Threshold uncertainty score0.497

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.049
GPT teacher head0.327
Teacher spread0.278 · 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