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Record W2002037832 · doi:10.5964/jspp.v3i1.117

Why Do Conservatives Report Being Happier Than Liberals? The Contribution of Neuroticism

2015· article· en· W2002037832 on OpenAlexafffund
Caitlin M. Burton, Jason E. Plaks, Jordan B. Peterson

Bibliographic record

VenueJournal of Social and Political Psychology · 2015
Typearticle
Languageen
FieldPsychology
TopicPsychological Well-being and Life Satisfaction
Canadian institutionsUniversity of Toronto
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsNeuroticismConscientiousnessReligiosityPsychologyHappinessConservatismSocial psychologyBig Five personality traitsBiology and political orientationPersonalityExtraversion and introversionPoliticsDevelopmental psychologyPolitical science

Abstract

fetched live from OpenAlex

Previous studies suggest that conservatives in the United States are happier than liberals. This difference has been attributed to factors including differences in socioeconomic status, group memberships, and system-justifying beliefs. We suggest that differences between liberals and conservatives in personality traits may provide an additional account for the "happiness gap". Specifically, we investigated the role of neuroticism (or conversely, emotional stability) in explaining the conservative-liberal happiness gap. In Study 1 (N = 619), we assessed the correlation between political orientation (PO) and satisfaction with life (SWL), controlling for the Big Five traits, religiosity, income, and demographic variables. Neuroticism, conscientiousness, and religiosity each accounted for the PO-SWL correlation. In Study 2 (N = 700), neuroticism, system justification beliefs, conscientiousness, and income each accounted for PO-SWL correlation. In both studies, neuroticism negatively correlated with conservatism. We suggest that individual differences in neuroticism represent a previously under-examined contributor to the SWL disparity between conservatives and liberals.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.591
Threshold uncertainty score0.388

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.068
GPT teacher head0.401
Teacher spread0.333 · 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

Citations83
Published2015
Admission routes2
Has abstractyes

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