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Record W2900834987 · doi:10.17615/qfe9-3916

Too tired to tell the truth: Self-control resource depletion and dishonesty

2009· preprint· en· W2900834987 on OpenAlexfundno aff
Nicole L. Mead, Roy F. Baumeister, Francesca Gino, Maurice E. Schweitzer, Dan Ariely

Bibliographic record

VenueResearch portal (Tilburg University) · 2009
Typepreprint
Languageen
FieldSocial Sciences
TopicExperimental Behavioral Economics Studies
Canadian institutionsnot available
FundersSocial Sciences and Humanities Research Council of CanadaJohn Templeton Foundation
KeywordsTemptationDishonestyCheatingHonestySelf-controlSocial psychologyControl (management)PsychologyEgo depletionResource depletionEconomics

Abstract

fetched live from OpenAlex

The opportunity to profit from dishonesty evokes a motivational conflict between the temptation to cheat for selfish gain and the desire to act in a socially appropriate manner. Honesty may depend on self-control given that self-control is the capacity that enables people to override antisocial selfish responses in favor of socially desirable responses. Two experiments tested the hypothesis that dishonesty would increase when people's self-control resources were depleted by an initial act of self-control. Depleted participants misrepresented their performance for monetary gain to a greater extent than did non-depleted participants (Experiment 1). Perhaps more troubling, depleted participants were more likely than non-depleted participants to expose themselves to the temptation to cheat, thereby aggravating the effects of depletion on cheating (Experiment 2). Results indicate that dishonesty increases when people's capacity to exert self-control is impaired, and that people may be particularly vulnerable to this effect because they do not predict it.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.706
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
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 head0.061
GPT teacher head0.351
Teacher spread0.290 · 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 designNot applicable
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

Citations0
Published2009
Admission routes1
Has abstractyes

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