The association between testosterone and unethical behaviours, and the moderating role of intrasexual competition
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".