Once a Poacher Always a Poacher? Mate Poaching History and its Association with Relationship Quality
Bibliographic record
Abstract
Successful mate poaching is a form of infidelity that occurs when one partner knowingly attracts the mate of another with the intention of starting a sexual and/or romantic relationship with this individual. Relationships formed from poaching tend to be of lower quality than their non-poached counterparts. A history of poaching might reflect a sociosexuality that propels seeking new partnerships without regard for exclusivity. It is unknown whether serial poaching for relationship formation is linked to more permissive sociosexual orientation. Adults (N = 653; aged 25–40; 57% women) in a romantic relationship completed online surveys assessing mate poaching, poaching history, sociosexuality, and relationship quality (commitment, satisfaction, trust, jealousy). Those in a poached relationship at the time of the study had a more extensive history of poached relationships and a more permissive sociosexuality. Participants who reported a more extensive history of mate poaching reported poorer quality relationships. The link between poaching history and relationship quality was partially accounted for by sociosexuality. This research adds to our understanding of difficulties that may be associated with the relationships of individuals who use poaching as a relationship initiation strategy, and the challenges that permissive sociosexuality may present for maintaining long-term relationships.
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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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".