Drawing the Line: The Development of a Comprehensive Assessment of Infidelity Judgments
Bibliographic record
Abstract
Infidelity is a leading cause of relationship discord and dissolution, and couples generally report expectations to maintain monogamy. However, a majority of men and women report engaging in some form of infidelity at least once in their lives. Research assessing judgments of the behaviors that constitute infidelity is lacking. The three studies reported here advanced the literature by developing and validating the Definitions of Infidelity Questionnaire (DIQ), a comprehensive measure examining infidelity judgments. Exploratory and confirmatory factor analyses indicated four factors to the scale: sexual/explicit behaviors, technology/online behaviors, emotional/affectionate behaviors, and solitary behaviors. Investigation of the psychometric properties demonstrated the DIQ to be reliable and valid. Participants agreed that sexual/explicit behaviors comprised infidelity to the largest extent, whereas other types of behaviors (technology/online behaviors, emotional/affectionate behaviors, and solitary behaviors) were judged as comprising infidelity to a lesser extent. Men reported more permissive judgments than did women. This study provides insights regarding operationalizing infidelity and identifying areas of ambiguity and consensus. Implications of the findings for educators and practitioners working with individuals in intimate relationships 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 | 0.020 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".