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Record W1967989266 · doi:10.1037/0022-3514.95.5.1180

For better and for worse: Everyday social comparisons between romantic partners.

2008· article· en· W1967989266 on OpenAlexafffund
Rebecca T. Pinkus, Penelope Lockwood, Ulrich Schimmack, Marc A. Fournier

Bibliographic record

VenueJournal of Personality and Social Psychology · 2008
Typearticle
Languageen
FieldPsychology
TopicAttachment and Relationship Dynamics
Canadian institutionsThe Scarborough HospitalAmgen (Canada)University of Toronto
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsPsychologySpouseExperience sampling methodSocial psychologyRomanceEmpathySocial comparison theoryDevelopmental psychologyInterpersonal relationshipSocial relation

Abstract

fetched live from OpenAlex

The authors examined the frequency, direction, and impact of social comparisons between romantic partners. Comparisons were expected to occur on a daily basis, owing to regular interactions between partners. To the extent that one empathizes and shares outcomes with one's partner, one might respond more positively to upward than to downward comparisons. Study 1a was an experience-sampling study in which participants reported comparisons made to their spouse over 2 weeks. Study 1b examined reactions to the most significant comparisons made during the experience-sampling study. Participants reported making comparisons to their romantic partner more than once a day on average and experienced more positive responses to upward than to downward comparisons. Study 2 demonstrated that participants empathized and shared outcomes with their partner to a greater extent than with a friend. Study 3 confirmed that participants responded more positively to upward than to downward comparisons even for domains high in self-relevance and even when the comparison had negative self-evaluative implications. These results suggest that, owing to higher levels of empathy and shared fate with partners, comparisons function differently in romantic than in other 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)0.001
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 score0.118
Threshold uncertainty score0.575

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.188
GPT teacher head0.504
Teacher spread0.316 · 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

Citations85
Published2008
Admission routes2
Has abstractyes

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