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Record W4225080362 · doi:10.1177/01461672221089451

People See Political Opponents as More Stupid Than Evil

2022· article· en· W4225080362 on OpenAlexaff
Rachel Hartman, Neil Hester, Kurt Gray

Bibliographic record

VenuePersonality and Social Psychology Bulletin · 2022
Typearticle
Languageen
FieldNeuroscience
TopicPsychology of Moral and Emotional Judgment
Canadian institutionsMcGill University
FundersCharles Koch Foundation
KeywordsPoliticsSocial psychologyPerceptionDemocracyPsychologyNegativity effectSociologyLawPolitical science

Abstract

fetched live from OpenAlex

Affective polarization is a rising threat to political discourse and democracy. Public figures have expressed that “conservatives think liberals are stupid, and liberals think conservatives are evil.” However, four studies ( N = 1,660)—including a representative sample—reveal evidence that both sides view political opponents as more unintelligent than immoral. Perceiving the other side as “more stupid than evil” occurs both in general judgments (Studies 1, 3, and 4) and regarding specific issues (Study 2). Study 4 also examines “meta-perceptions” of how Democrats and Republicans disparage one another, revealing that people correctly perceive that both Democrats and Republicans see each other as more unintelligent than immoral, although they exaggerate the extent of this negativity. These studies clarify the way everyday partisans view each other, an important step in designing effective interventions to reduce political animosity.

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)0.002
metaresearch head score (Gemma)0.008
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 score0.003
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0030.002
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.117
GPT teacher head0.351
Teacher spread0.235 · 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

Citations46
Published2022
Admission routes1
Has abstractyes

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