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Record W4388851114 · doi:10.1002/acp.4154

Not all bullshit pondered is tossed: Reflection decreases receptivity to some types of misleading information but not others

2023· article· en· W4388851114 on OpenAlexafffund
Shane Littrell, Ethan Andrew Meyers, Jonathan A. Fugelsang

Bibliographic record

VenueApplied Cognitive Psychology · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicMisinformation and Its Impacts
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMisinformationPsychologySuspectAppealReflection (computer programming)Psychological interventionReceptivitySocial psychologyEpistemologyLawCriminologyPhilosophyComputer science

Abstract

fetched live from OpenAlex

Abstract Across three studies ( N = 659), we present evidence that engaging in explanatory reflection reduces receptivity to pseudo‐profound bullshit but not scientific bullshit or fake news. Additionally, ratings for pseudo‐profound and scientific bullshit attributed to authoritative sources were significantly inflated compared to bullshit from anonymous sources. These findings provide initial evidence that asking people to reflect on why they find certain statements meaningful (or not) helps reduce receptivity to some types of misinformation but not others. Moreover, the appeal of misleading claims spread by perceived experts may be largely immune to the putative benefits of interventions that rely solely on reflective thinking. Taken together, our results suggest that while encouraging the public to be more reflective can certainly be helpful as a general rule, the effectiveness of this strategy in reducing the persuasiveness of misleading or otherwise epistemically‐suspect claims is limited by the type of claims being evaluated.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.426
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.003

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 head0.089
GPT teacher head0.406
Teacher spread0.317 · 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.

Study designQualitative
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

Citations3
Published2023
Admission routes2
Has abstractyes

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