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Cultural Differences and Similarities in the Nature of Infidelity

2022· book-chapter· en· W4307699032 on OpenAlexaff
Farid Pazhoohi

Bibliographic record

VenueOxford University Press eBooks · 2022
Typebook-chapter
Languageen
FieldPsychology
TopicEvolutionary Psychology and Human Behavior
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsNeglectDiversity (politics)PsychologySocial psychologyCultural diversityDistressSociologyAnthropologyClinical psychology

Abstract

fetched live from OpenAlex

Abstract This chapter highlights the variations and diversity of human mating systems and cultural differences and similarities in the nature of and attitudes toward infidelity. Altogether, this chapter reviews and shows what is constituted as infidelity in one culture might not necessarily be considered as infidelity in another culture. While there has been some cross-cultural research to evaluate differences and similarities across societies and populations in infidelity, the literature still lacks proper research on what is considered as infidelity in different cultures, societies, and traditions. The current issues of research such as lack of diversity, (i.e., sample limitation to heterosexual, middle-to-upper-class, white, undergraduate students, from Western and industrialized societies, majority from the United States) are noted. Moreover, this chapter argues that the intense interest among behavioral researchers in identifying a universal sex difference in distress over sexual and emotional aspects of infidelity has resulted in neglect of exploring the nature of infidelity and the cultural variations in the attitudes toward infidelity. Finally, by signifying a limited research that employed a behavioral ecological approach, this chapter calls for cross-cultural research based on a behavioral ecological approach on cultural differences and similarities in the nature of infidelity.

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 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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.987
Threshold uncertainty score0.894

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.047
GPT teacher head0.280
Teacher spread0.232 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreOther

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

Citations6
Published2022
Admission routes1
Has abstractyes

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