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Record W3190036396 · doi:10.1111/bjop.12525

The association between testosterone and unethical behaviours, and the moderating role of intrasexual competition

2021· article· en· W3190036396 on OpenAlexaff
Marcelo Vinhal Nepomuceno, Eric Stenstrom

Bibliographic record

VenueBritish Journal of Psychology · 2021
Typearticle
Languageen
FieldPsychology
TopicEvolutionary Psychology and Human Behavior
Canadian institutionsHEC Montréal
Fundersnot available
KeywordsPsychologyTestosterone (patch)AngerSexual selectionCompetition (biology)Social psychologyDevelopmental psychologyClinical psychologyEcologyEndocrinology

Abstract

fetched live from OpenAlex

Researchers have called for a greater use of neuroscientific methods to advance theories in ethical behaviour. Our research takes a neuroscientific approach to investigating unethical behaviour by examining the roles of testosterone and intrasexual competition. We propose that unethical behavioural intentions will be greater for high-testosterone individuals in response to highly intrasexually competitive situations as a means of enhancing status. In an experiment, we measure baseline testosterone and assign participants to an intrasexually competitive or control condition. We demonstrate that in men, but not in women, testosterone is positively associated with unethical behavioural intentions in response to an intrasexual competition prime. Furthermore, using textual analysis, we find that testosterone is positively associated with the usage of anger-related words in response to an intrasexual competition prime among men. In turn, anger-related words are positively associated with unethical behaviour, suggesting that anger may play a role in motivating high-testosterone men to behave unethically. Overall, our findings contribute to the literature by suggesting that testosterone and competition lead to greater unethical behaviour in men, and that anger plays a role in promoting unethical behaviour.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.237
Threshold uncertainty score0.379

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.022
GPT teacher head0.326
Teacher spread0.304 · 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 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

Citations3
Published2021
Admission routes1
Has abstractyes

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