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Record W2143621472 · doi:10.1177/0022022110385233

Does Understanding Behavior Make It Seem Normal?

2010· article· en· W2143621472 on OpenAlexaff
Lauren Ban, Yoshihisa Kashima, Nick Haslam

Bibliographic record

VenueJournal of Cross-Cultural Psychology · 2010
Typearticle
Languageen
FieldPsychology
TopicCultural Differences and Values
Canadian institutionsConcordia University
Fundersnot available
KeywordsAbnormalityDeviance (statistics)FallacyPsychologySocial psychologySalience (neuroscience)PerceptionDevelopmental psychologyCultural diversityCognitive psychologyEpistemologySociology

Abstract

fetched live from OpenAlex

According to recent research, abnormal behavior appears normal to the extent it is understood. Cultural differences in frameworks for making sense of abnormality suggest there may be variations in this “reasoning fallacy.” In light of evidence that people from Western cultures psychologize abnormality to a greater extent than people from East Asian cultures, the effect of understanding on perceptions of abnormality was predicted to differ across cultures. Results of a cross-cultural questionnaire study indicated that understanding made behavior seem normal to European Australians ( n = 51), consistent with the reasoning fallacy. For Singaporeans ( n = 51), however, understanding did not influence the extent to which behavior was normalized and made abnormal behavior more stigmatizing. Cultural variations in the effect of understanding were attributed to the differential salience of deviance frameworks, which are grounded in culturally specific conceptions of the person.

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)0.007
metaresearch head score (Gemma)0.059
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 score0.007
Threshold uncertainty score0.035

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.059
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.009
Scholarly communication0.0020.004
Open science0.0010.002
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0020.000

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 head0.173
GPT teacher head0.493
Teacher spread0.320 · 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

Citations13
Published2010
Admission routes1
Has abstractyes

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