Bibliographic record
Abstract
This is a narrative review addressing the topic of romantic infidelity, its causes and its consequences. Love is commonly a source of much pleasure and fulfillment. However, as this review points out, it can also cause stress, heartache and may even be traumatic in some circumstances. Infidelity, which is relatively common in Western culture, can damage a loving, romantic relationship to the point of its demise. However, by highlighting this phenomenon, its causes and its consequences, we hope to provide useful insight for both researchers and clinicians who may be assisting couples facing these issues. We begin by defining infidelity and illustrating the various ways in which one may become unfaithful to their partner. We explore the personal and relational factors that enhance an individual's tendency to betray their partner, the various reactions related to a discovered affair and the challenges related to the nosological categorization of infidelity-based trauma, and conclude by reviewing the effects of COVID-19 on unfaithful behavior, as well as clinical implications related to infidelity-based treatment. Ultimately, we hope to provide a road map, for academicians and clinicians alike, of what some couples may experience in their relationships and how can they be helped.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".