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Record W2782760876 · doi:10.1111/jopy.12370

Is it really “all in their heads”? How self‐esteem predicts partner responsiveness

2018· article· en· W2782760876 on OpenAlexafffund
Kassandra Cortes, Joanne V. Wood

Bibliographic record

VenueJournal of Personality · 2018
Typearticle
Languageen
FieldPsychology
TopicAttachment and Relationship Dynamics
Canadian institutionsUniversity of Waterloo
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsPsychologySelf-esteemPerceptionFeelingDevelopmental psychologySocial psychologyClinical psychology

Abstract

fetched live from OpenAlex

Abstract Objective Having a responsive partner is important for the well‐being of relationships. Unfortunately, people with low self‐esteem (LSEs) perceive their partners to be less responsive than do people with high self‐esteem (HSEs). Although the common assumption has been that LSEs' negative partner perceptions are “all in their heads”—a reflection of their negative self‐projection—we argue that LSEs' views of lower partner responsiveness are, in fact, warranted. Method Across two studies ( N Study1 = 122 couples, M age = 22.28, 50% female; N Study2 = 73 couples, M age = 19.96, 51% female), we examined LSEs' and HSEs' perceptions of their partners' responsiveness to their negative self‐disclosures, comparing them with partners' reports (Study 1) and ratings from objective coders following a negative experience created in the lab (Study 2). Results Consistent with our hypothesis, partners of LSEs were less responsive than partners of HSEs to disclosers' negative self‐disclosures, as rated by disclosers, listeners, and objective observers. Study 3 ( N = 99, M age = 33.19, 54% female) explored possible mechanisms behind these self‐esteem differences. Conclusions The finding that partners of LSEs (vs. HSEs) are less responsive may contribute to LSEs' poorer 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)0.002
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.053
Threshold uncertainty score0.843

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 integrity0.0000.001
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 head0.104
GPT teacher head0.439
Teacher spread0.334 · 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

Citations19
Published2018
Admission routes2
Has abstractyes

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