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Record W2105630032 · doi:10.1177/0956797613492425

The Ergonomics of Dishonesty

2013· article· en· W2105630032 on OpenAlex
Andy J. Yap, Abbie S. Wazlawek, Brian J. Lucas, Amy J. C. Cuddy, Dana R. Carney

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenuePsychological Science · 2013
Typearticle
Languageen
FieldPsychology
TopicBehavioral Health and Interventions
Canadian institutionsKellogg's (Canada)
FundersNational Science Foundation
KeywordsExpansiveDishonestyCommitFeelingPsychologySocial psychologyAction (physics)Human factors and ergonomicsPower (physics)Poison controlComputer scienceMedical emergency

Abstract

fetched live from OpenAlex

Research in environmental sciences has found that the ergonomic design of human-made environments influences thought, feeling, and action. In the research reported here, we examined the impact of physical environments on dishonest behavior. In four studies, we tested whether certain bodily configurations-or postures-incidentally imposed by the environment led to increases in dishonest behavior. The first three experiments showed that individuals who assumed expansive postures (either consciously or inadvertently) were more likely to steal money, cheat on a test, and commit traffic violations in a driving simulation. Results suggested that participants' self-reported sense of power mediated the link between postural expansiveness and dishonesty. Study 4 revealed that automobiles with more expansive driver's seats were more likely to be illegally parked on New York City streets. Taken together, the results suggest that, first, environments that expand the body can inadvertently lead people to feel more powerful, and second, these feelings of power can cause dishonest behavior.

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.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.948
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
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 head0.098
GPT teacher head0.462
Teacher spread0.364 · 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