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Record W4402321737 · doi:10.1111/pere.12570

Self‐concept clarity and the evaluation and selection of incompatible dating partners

2024· article· en· W4402321737 on OpenAlexafffund
Katya F. Kredl, Dita Kubin, John E. Lydon

Bibliographic record

VenuePersonal Relationships · 2024
Typearticle
Languageen
FieldPsychology
TopicAttachment and Relationship Dynamics
Canadian institutionsMcGill University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsCLARITYPsychologySelection (genetic algorithm)Social psychologyComputer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

Abstract Romantic compatibility is theorized to play a crucial role in the success and stability of romantic relationships, although evidence to date has been somewhat indirect. Previous experimental findings suggest that those lower, relative to higher, in self‐concept clarity find it more challenging to evaluate similarity (i.e., a contributor to compatibility) in prospective romantic partners. The current research extends these findings by directly examining self‐concept clarity and romantic partner (in)compatibility in real‐world experiences. Across two retrospective studies ( N = 340), we found that those lower, relative to higher, in self‐concept clarity dated incompatible others more frequently, experienced greater difficulty judging compatibility, and were less decisive in their dating decisions. They also experienced greater dating‐related negative affect but did not report lower satisfaction in past dating. Exploratory mediation analyses further suggest that such individuals experienced greater dating‐related negative affect through dating incompatible others more often. Specifically, they were more likely to date incompatible others if they found it harder to judge compatibility and were less decisive in dating. These results suggest that individuals with a confused personal identity (i.e., low in self‐concept clarity) may find it more challenging to evaluate potential dating partners, leading them to rule out incompatible ones less often.

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)0.002
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.581
Threshold uncertainty score0.275

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.084
GPT teacher head0.441
Teacher spread0.357 · 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 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

Citations3
Published2024
Admission routes2
Has abstractyes

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