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Record W2526394992 · doi:10.1017/s1930297500003788

Are neoliberals more susceptible to bullshit?

2016· article· en· W2526394992 on OpenAlexaff
Joanna Sterling, John T. Jost, Gordon Pennycook

Bibliographic record

VenueJudgment and Decision Making · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicSocial and Intergroup Psychology
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsIdeologyCertaintyCognitive styleCognitionPsychologyIntuitionBetrayalStyle (visual arts)Social psychologyFaithSociologyEpistemologyPoliticsPolitical sciencePhilosophyCognitive scienceLiteratureArtLaw

Abstract

fetched live from OpenAlex

Abstract We conducted additional analyses of Pennycook et al.’s (2015, Study 2) data to investigate the possibility that there would be ideological differences in “bullshit receptivity” that would be explained by individual differences in cognitive style and ability. As hypothesized, we observed that endorsement of neoliberal, free market ideology was significantly but modestly associated with bullshit receptivity. In addition, we observed a quadratic association, which indicated that ideological moderates were more susceptible to bullshit than ideological extremists. These relationships were explained, in part, by heuristic processing tendencies, faith in intuition, and lower verbal ability. Results are inconsistent with approaches suggesting that (a) there are no meaningful ideological differences in cognitive style or reasoning ability, (b) simplistic, certainty-oriented cognitive styles are generally associated with leftist (vs. rightist) economic preferences, or (c) simplistic, certainty-oriented cognitive styles are generally associated with extremist (vs. moderate) preferences. Theoretical and practical implications are briefly addressed.

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)0.003
metaresearch head score (Gemma)0.014
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 score0.006
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.014
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.000

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 head0.051
GPT teacher head0.390
Teacher spread0.339 · 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

Citations98
Published2016
Admission routes1
Has abstractyes

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