Between the Lines of Betrayal: An Examination of a Large-Scale Survey on Sex Differences in Infidelity Behaviors, Motivations, and Outcomes
Bibliographic record
Abstract
The Truth About Deception website provides information on romantic and sexual relationships.In addition, visitors may complete anonymous, short quizzes.Here we focus on the responses from a quiz on infidelity completed by 94,943 (66.1% women, 33.9% men) individuals.The items pertain to personal experiences with infidelity, motivations for engaging in infidelity, and the outcomes of infidelity.Analyses based on odds ratios showed that women are significantly more likely to say they have had an emotional affair, engaged in infidelity because they were bored with their sex life, took part in cybersex, used online sources to have infidelity, became involved in infidelity when there were problems in the relationship, engaged in an infidelity with someone their spouse knew, and considered leaving their spouse because of their infidelity.In contrast, men were significantly more likely to engage in sexual infidelity and do so more than once.Six additional items dealt with how the spouse 'discovered the truth,' indicating that most individuals (56.8%) confessed independently.We discuss these findings from an evolutionary perspective, focusing on sex differences in infidelity experiences arising from sex-specific dilemmas faced by humans over evolutionary time.To the best of our knowledge, these data represent an untapped source of information whereby evolutionary-based predictions may be tested.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".