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Record W2945319691 · doi:10.1080/00224499.2019.1610150

Once a Poacher Always a Poacher? Mate Poaching History and its Association with Relationship Quality

2019· article· en· W2945319691 on OpenAlexaff
Charlene F. Belu, Lucia F. O’Sullivan

Bibliographic record

VenueThe Journal of Sex Research · 2019
Typearticle
Languageen
FieldPsychology
TopicEvolutionary Psychology and Human Behavior
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsPoachingPermissivePsychologyJealousySocial psychologyRomanceQuality (philosophy)Developmental psychologyEcologyBiology

Abstract

fetched live from OpenAlex

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.

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)0.014
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.052
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0140.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
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 head0.271
GPT teacher head0.459
Teacher spread0.189 · 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.

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

Citations14
Published2019
Admission routes1
Has abstractyes

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