Recalling, reacting but not so much regretting: How young adults describe their sexual and romantic infidelity experiences
Bibliographic record
Abstract
Infidelity is reported at high rates despite strong societal prohibitions against it, leading to questions about whether outcomes support the motives driving infidelity. Little is known about whether motives behind infidelity correspond to perceived outcomes, including regret, but such information might help to explain the paradox of the high rates. Participants were recruited from a large prospective study on monogamy. Analyses were conducted on surveys from the 94 individuals who engaged in infidelity over the year. Using structured and open-ended measures, the authors examined how infidelity evolved, patterns among motives and outcomes, and regret. Infidelity typically began at work or online, lasted about one year, and involved sex as well as feelings of infatuation or love. Most (63.4%) reported not regretting their infidelity. Motives (anger, neglect, dissatisfaction, sex) were compared with outcomes (fulfilled needs, sexual satisfaction, distress) to assess concordance. Being motivated by feelings of neglect or relationship dissatisfaction was associated with needs fulfilled by infidelity; sex as a motive was associated with sexual satisfaction as an outcome. However, concordance in motives and outcomes was unrelated to regret.
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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 | 0.009 |
| 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 | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".