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Record W2058522085 · doi:10.1177/02654075030201006

Self- and Partner-Perceptions of Interpersonal Problems and Relationship Functioning

2003· article· en· W2058522085 on OpenAlexaff
Colleen Saffrey, Kim Bartholomew, Elaine Scharfe, Antonia J. Z. Henderson, Ray Koopman

Bibliographic record

VenueJournal of Social and Personal Relationships · 2003
Typearticle
Languageen
FieldPsychology
TopicAttachment and Relationship Dynamics
Canadian institutionsTrent UniversitySimon Fraser University
Fundersnot available
KeywordsPsychologyInterpersonal communicationPerceptionInterpersonal relationshipQuality (philosophy)Social psychologyDevelopmental psychologyClinical psychology

Abstract

fetched live from OpenAlex

This study investigated whether self- and partner-perceptions of interpersonal problems predicted relationship functioning. Partners’ understanding of one another’s interpersonal problems, projection of their own problems onto their partners, and positive perceptions of their partners’ problems were assessed. Individuals from 76 couples completed selfreport measures of their own interpersonal problems and of their perceptions of their partners’ interpersonal problems. Relationship functioning was assessed by self-reported satisfaction and by expert ratings of relationship quality. Partner-perceptions more strongly and consistently predicted relationship functioning than did self-perceptions. There was evidence of understanding of interpersonal problems, but degree of understanding did not predict relationship satisfaction or quality. Similarly, there was evidence of projection of level of interpersonal problems, but degree of projection did not predict relationship functioning. In contrast, positive perceptions of interpersonal problems were associated with positive relationship functioning.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.068
GPT teacher head0.361
Teacher spread0.293 · 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 source (direct Gemma or distilled Codex), 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

Citations46
Published2003
Admission routes1
Has abstractyes

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