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Record W4284886286 · doi:10.1177/02654075221113032

“I’ve been cheated, been mistreated, when will I be loved”: Two decades of infidelity research through an intersectional lens

2022· article· en· W4284886286 on OpenAlex
Dana A. Weiser, M. Rosie Shrout, Adam V. Thomas, Adrienne L. Edwards, Jaclyn D. Cravens

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueJournal of Social and Personal Relationships · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicMarriage and Sexual Relationships
Canadian institutionsnot available
Fundersnot available
KeywordsHeterosexismConceptualizationPsychologySocial psychologyIntersectionalityInterpersonal communicationContext (archaeology)RomanceIdentity (music)RacismWhite (mutation)Gender studiesSociologyHomosexualityPsychoanalysis

Abstract

fetched live from OpenAlex

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.233
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0040.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.306
GPT teacher head0.437
Teacher spread0.131 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it