Bibliographic record
Abstract
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.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".