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Record W2144724125 · doi:10.1017/s000842390707117x

The Effects of Information and Social Cleavages: Explaining Issue Attitudes and Vote Choice in Canada

2007· article· en· W2144724125 on OpenAlexaffabout
Amanda Bittner

Bibliographic record

VenueCanadian Journal of Political Science · 2007
Typearticle
Languageen
FieldSocial Sciences
TopicElectoral Systems and Political Participation
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsSocial identity theoryPoliticsSocial groupIdentity (music)Collective identitySocial psychologyPolitical scienceVotingSurvey data collectionPublic opinionSociologyPsychologyLaw

Abstract

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Abstract. This paper examines the relationship between social group identity and the level of political information in explaining Canadians' issue attitudes and vote choices. Traditional accounts of Canadians' partisan political leanings have placed a great deal of emphasis on social group identity in explaining attitudes. However based on data from the Canadian Election Studies from 1988–2004, it is argued that both social group identity and information influence the nature of vote choice and public opinion in Canada. In fact, the level of voter information has two contradictory effects on the political attitudes of different social groups. In some cases voters' level of information reduces the role of social group identity in explaining attitudes and vote choices; information acts to bridge the differences between different social groups (for example Catholics/non-Catholics and urban/rural Canadians). In other cases, voters' level of information acts to amplify the importance of social group identity in predicting attitudes (for example women/men and religious/nonreligious). These findings suggest that not only is social group identity a less effective predictor of attitudes than has traditionally been thought but that there are also significant underlying differences between the so-called “old” and “new” cleavages in Canada when it comes to understanding their impact on political values. Résumé. Cet article examine le lien qui existe entre l'appartenance à un groupe social donné et le niveau d'information politique lorsqu'il s'agit d'expliquer l'attitude des Canadiens et des Canadiennes devant certaines questions d'intérêt politique et leur choix de vote. Les analyses traditionnelles des tendances partisanes au Canada mettent surtout l'accent sur l'appartenance à un groupe social particulier pour expliquer les positions. Toutefois, en nous fondant sur les données des Études électorales canadiennes de 1988 à 2004, nous soutenons que l'appartenance au groupe social tout comme le niveau d'information exercent une influence sur le choix de vote et sur l'opinion publique au Canada. En réalité, le niveau d'information des électeurs produit deux effets contradictoires sur la position politique de groupes sociaux différents. Dans certains cas, le niveau d'information des électeurs affaiblit le rôle du groupe social dans l'explication des positions et des choix de vote; il permet d'outrepasser les distinctions de groupe social (par exemple catholiques vs non-catholiques et Canadiens en milieu urbain ou en milieu rural). Dans d'autres cas, le niveau d'information amplifie l'importance du groupe social comme facteur permettant de prédire les positions (par exemple femmes vs hommes et électeurs religieux ou non religieux). Ces découvertes suggèrent que pour prédire les positions politiques, le groupe social est un indicateur moins fiable que ne le supposent les analyses traditionnelles. De surcroît, il existe d'importantes différences sous-jacentes entre les soi-disant “anciens” et “nouveaux” clivages au Canada lorsqu'il s'agit d'interpréter leur incidence sur les valeurs politiques.

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)0.002
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.561
Threshold uncertainty score0.805

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
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 head0.015
GPT teacher head0.315
Teacher spread0.300 · 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 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

Citations19
Published2007
Admission routes2
Has abstractyes

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