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Record W2951175175 · doi:10.1177/0265407519856701

The predictive effects of fear of being single on physical attractiveness and less selective partner selection strategies

2019· article· en· W2951175175 on OpenAlexafffund
Stephanie S. Spielmann, Jessica A. Maxwell, Geoff MacDonald, Diana E. Peragine, Emily A. Impett

Bibliographic record

VenueJournal of Social and Personal Relationships · 2019
Typearticle
Languageen
FieldPsychology
TopicEvolutionary Psychology and Human Behavior
Canadian institutionsUniversity of Toronto
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsPsychologyPhysical attractivenessAttractivenessBivariate analysisSocial psychologyAssociation (psychology)Interpersonal attractionAttractionDevelopmental psychologyStatistics

Abstract

fetched live from OpenAlex

Fear of being single (FOBS) tends to predict settling for less when seeking a romantic partner. The present research sought to examine whether this is due, at least in part, to lower physical attractiveness among those who fear being single. In a photo-rating study (Study 1, N = 122) and a speed-dating study (Study 2, N = 171), participants completed the FOBS Scale, rated perceptions of their own physical attractiveness, and were then rated on physical attractiveness by a team of raters. In Studies 1 and 2, FOBS was not significantly associated with judge-rated physical attractiveness as a bivariate association or in hierarchical regressions accounting for anxious and avoidant attachments, gender, and smiling. There were mixed findings in both studies regarding the association between FOBS and self-rated physical attractiveness in bivariate versus multivariate analyses. However, the tendency of those with stronger FOBS to be less selective during speed dating was not explained by either their judge-rated or their self-rated physical attractiveness.

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)0.000
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.373
Threshold uncertainty score0.252

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.051
GPT teacher head0.341
Teacher spread0.290 · 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

Citations39
Published2019
Admission routes2
Has abstractyes

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