The relative importance of contextual factors in judging mundane extradyadic behaviors as infidelity: A policy‐capturing study
Bibliographic record
Abstract
Abstract Seemingly benign extradyadic behaviors (e.g., buying/receiving gifts or talking on the phone) may be perceived as infidelity under certain circumstances, therefore causing distress and conflict in romantic relationships. A policy‐capturing method was used to illuminate the relative role of contextual factors (secrecy, frequency of the behavior, and the victim's familiarity with the rival) in perceiving whether a mundane, everyday extradyadic act is perceived to cross the line from benign to infidelity. In two sessions, 135 participants completed individual difference measures and rated 30 different vignettes in which the extradyadic behavior (i.e., direct messaging on social media) was held constant, but levels of contextual factors varied. Participants perceived secrecy as the most important contextual factor in deciding whether a mundane extradyadic behavior constituted infidelity, followed by frequency. The victim's familiarity with the rival was deemed least important. Higher reactive jealousy predicted higher perception of the behavior as infidelity and greater anticipated emotional distress following the behavior. Implications for how couples discuss the boundaries of their relationships and understand the source of emotional distress experienced after seemingly benign extradyadic behaviors are discussed.
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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 | 0.005 | 0.022 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".