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Record W3016977417 · doi:10.1111/bjso.12379

The Bullshitting Frequency Scale: Development and psychometric properties

2020· article· en· W3016977417 on OpenAlexafffund
Shane Littrell, Evan F. Risko, Jonathan A. Fugelsang

Bibliographic record

VenueBritish Journal of Social Psychology · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicMisinformation and Its Impacts
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsSincerityPsychologyHonestyScale (ratio)Need for cognitionCognitionSocial psychologyCognitive psychology

Abstract

fetched live from OpenAlex

Recent psychological research has identified important individual differences associated with receptivity to bullshit, which has greatly enhanced our understanding of the processes behind susceptibility to pseudo-profound or otherwise misleading information. However, the bulk of this research attention has focused on cognitive and dispositional factors related to bullshit (the product), while largely overlooking the influences behind bullshitting (the act). Here, we present results from four studies focusing on the construction and validation of a new, reliable scale measuring the frequency with which individuals engage in two types of bullshitting (persuasive and evasive) in everyday situations. Overall, bullshitting frequency was negatively associated with sincerity, honesty, cognitive ability, open-minded cognition, and self-regard. Additionally, the Bullshitting Frequency Scale was found to reliably measure constructs that are (1) distinct from lying and (2) significantly related to performance on overclaiming and social decision tasks. These results represent an important step forward by demonstrating the utility of the Bullshitting Frequency Scale as well as highlighting certain individual differences that may play important roles in the extent to which individuals engage in everyday bullshitting.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.865
Threshold uncertainty score0.978

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 studies0.0010.000
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 head0.069
GPT teacher head0.335
Teacher spread0.267 · 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 designOther design
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

Citations52
Published2020
Admission routes2
Has abstractyes

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