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Record W2117356412 · doi:10.1177/147470490800600308

Sex Differences in Feelings of Guilt Arising from Infidelity

2008· article· en· W2117356412 on OpenAlexaff
Maryanne L. Fisher, Martin Voracek, P. Vivien Rekkas, A. D. Cox

Bibliographic record

VenueEvolutionary Psychology · 2008
Typearticle
Languageen
FieldPsychology
TopicEvolutionary Psychology and Human Behavior
Canadian institutionsThe Scarborough HospitalUniversity of TorontoSaint Mary's UniversitySt. Mary's University
Fundersnot available
KeywordsJealousyPsychologyFeelingSocial psychologyDevelopmental psychologyMating

Abstract

fetched live from OpenAlex

Although there is extensive literature regarding sex differences in jealousy due to infidelity, guilt resulting from infidelity remains unexplored. We hypothesize that men will feel guiltier from imagined emotional rather than sexual infidelity, as it is most important for their partner's reproductive success. Similarly, we predict that women will feel more guilt from imagined sexual rather than emotional infidelity. The findings indicate a different pattern; men feel guiltier following sexual infidelity, whereas women feel guiltier following emotional infidelity. Results also show that both sexes believe their partners would have a more difficult time forgiving sexual, rather than emotional, infidelity, but women and not men report that sexual infidelity would more likely lead to relationship dissolution. These findings are discussed in view of evolved mating strategies and individual reproductive success.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
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 head0.085
GPT teacher head0.357
Teacher spread0.272 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations31
Published2008
Admission routes1
Has abstractyes

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