How contingencies of self-worth influence reactions to emotional and sexual infidelity
Bibliographic record
Abstract
How do men and women interpret the meaning of sexual infidelities? Is it different from the way they interpret emotional infidelities? People make different attributions regarding infidelity depending on their self-worth. The influence of this intrapsychic factor on reactions to infidelity deserves greater study. Some people will construe infidelity as evidence of their partners' lack of trustworthiness. Others might attribute infidelity to situational factors beyond anyone's control, and avoid blaming their partners altogether. However, if one's sense of self-worth is highly contingent on external sources their attributions may change. In these cases, one may interpret infidelity to mean that others find him or her undesirable and unlovable. In the present study, we sought to investigate how self-worth might influence reactions to sexual versus emotional infidelity using the Contingencies of Self-Worth Scale (CSWS) and the Buss Jealousy Instrument. A chi square analysis was used to determine whether reactions to infidelity depended on sex and Hotelling's T-square test was used to determine whether CSWS domains were dependent on sex. Binomial logistic regressions were conducted to assess between-sex and within-sex differences in reactions to emotional versus sexual infidelity. There was no significant difference between men's and women's reactions to sexual versus emotional infidelity. Greater distress associated with sexual infidelity was found in men whose self-worth was contingent on competition, but this difference was not found in women. Clinicians may benefit from an awareness of how intrapsychic factors influence clients' reactions to infidelity.
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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 | 0.001 | 0.000 |
| 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".