“I’ve been cheated, been mistreated, when will I be loved”: Two decades of infidelity research through an intersectional lens
Bibliographic record
Abstract
Infidelity is a common experience within romantic relationships and is closely linked with relationship dissolution and well-being. Using an intersectionality theoretical framework, we undertook a systematic review of the infidelity literature in flagship journals associated with the disciplines of the International Association for Relationship Research. Our review includes findings from 162 published empirical articles. We identified several themes within the infidelity literature, including: individual, interpersonal, and contextual predictors; outcomes and reactions; beliefs and attitudes; prevalence; and conceptualization. We also found that the infidelity literature primarily utilizes participants who are White, heterosexual, cisgender individuals who reside in the United States or Canada. Moreover, researchers were limited in information they provided about participants’ identities so in most articles it was difficult to assess many dimensions of identity. Ultimately, these findings limit our ability to apply an intersectional framework. We argue that researchers should extend the research they cite, collect richer demographic data, expand their samples (especially beyond White heterosexual cisgender American college students), and consider the sociohistorical context of their participants (e.g., the particular social circumstances and historical forces which shape individuals’ lived experiences). For example, scholars using an intersectional framework would explain their participants’ relationship experiences through a lens which includes systems of sexism, racism, heterosexism, cissexism, classism, etc., in conjunction with individual and interpersonal factors.
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 | 0.025 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.013 | 0.013 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.010 | 0.019 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".