Bibliographic record
Abstract
Infidelity can render some of the most significant harm on an intimate relationship by threatening the foundations on which relationships thrive—namely, trust, confidence, and security. Infidelity is broadly defined as any form of in-person or online extradyadic behavior (eg, sexual, romantic, solitary) that violates exclusivity agreements between partners. Most believe that monogamy—the expectation to remain sexually and romantically exclusive—is a central feature of romantic relationships. Despite widely held expectations for monogamy, infidelity is highly prevalent. Data from nationally representative samples from the United States, Norway, Finland, Estonia, and Petersburg indicate that 13.3% to 37.5% of individuals in romantic relationships (often cohabiting/married) report engaging in sexual activity with someone other than their current partners in violation of an agreement to be monogamous.1 Research based on broader definitions of infidelity consistently find much higher prevalence rates.1,2 Moreover, infidelity costs are often extreme in terms of relationship, sexual, and psychological well-being for those who engage in infidelity and their partners, as well as their family and friends, with ramifications extending to education and employment spheres.2,3 Infidelity is one of the primary reasons that couples start therapy, but it is also a key reason that relationships dissolve.2 Because of the high prevalence and corresponding costs, researchers have made many efforts to predict who is at risk of engaging in infidelity to help offset harms to people in relationships and those working with couples in distress.2-4 In this expert opinion, we briefly discuss some key individual, relationship, and contextual predictors of infidelity among couples, although a more detailed account of this research can be found in reviews of the infidelity literature.2,3,5
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".