Why Find My Own When I Can Take Yours?: The Quality of Relationships That Arise From Successful Mate Poaching
Bibliographic record
Abstract
Mate poaching occurs when a person attracts another, whom he or she knows is already in an exclusive relationship, into a sexual or romantic relationship. Mate poaching is involved in the evolution of many relationships. Yet, little is known about the quality of these relationships. We examined relationship quality between individuals whose relationships were formed via mate poaching versus not (i.e., a relationship formed serially without overlap with another relationship). We compared ratings of quality from the perspectives of poachers, poached, and co-poached individuals. Adult participants (n = 660) in a romantic relationship responded to questions assessing relationship quality. Those in relationships formed from poaching rated their relationships as lower in relationship satisfaction, commitment and trust, and higher in jealousy, and had higher rates of romantic and sexuality infidelity in their current relationship compared to individuals in non-poached relationships. Those who were poached from an existing relationship rated their current relationship as lower in commitment than did those who poached their current partner into a relationship. The study also provides first insights regarding relationship quality for those who identify as co-poached. We discuss these findings in terms of implications for understanding how relationships are formed and the qualities of those that endure.
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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 | 0.003 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".