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Record W3137808545 · doi:10.33921/gqkl9209

Communication as a Mediator Between Personal Characteristics – Five-Factor Personality Traits, Emotional Intelligence, Self-Disclosure – and Romantic Relationship Satisfaction

2017· article· en· W3137808545 on OpenAlexvenueno aff
Sarah E. Mackay, Kenneth M. Cramer

Bibliographic record

VenueJournal of Interpersonal Relations Intergroup Relations and Identity · 2017
Typearticle
Languageen
FieldPsychology
TopicEmotional Intelligence and Performance
Canadian institutionsnot available
Fundersnot available
KeywordsConscientiousnessPsychologyBig Five personality traitsPersonalityEmotional intelligenceSelf-disclosureSocial psychologyRomancePopulationRelation (database)Developmental psychologyExtraversion and introversion

Abstract

fetched live from OpenAlex

The present study explored the relation between personal characteristics and romantic relationship satisfaction as mediated by communication. Couples in established heterosexual romantic relationships of at least 3 months (N = 96 couples) were recruited from an undergraduate population at a university through a Psychology Participant Pool System. It’s been hypothesized that there would be a relation between predicting variables — four of five-factor personality traits, emotional intelligence and self-disclosure — and relationship satisfaction as mediated by communication behaviours. Results indicate that for both genders, conscientiousness is related to one’s own relationship satisfaction which is mediated by communication. For females only, communication mediated the relation between emotional intelligence and her relationship satisfaction. For males and females, self-disclosure is related to both one’s own and one’s partner’s relationship satisfaction which is mediated by communication. Collectively, these results suggest that personal characteristics are related to communication which influences the relationship satisfaction of both members of a couple.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.048
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.001
Scholarly communication0.0000.003
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.036
GPT teacher head0.356
Teacher spread0.320 · 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.

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

Citations0
Published2017
Admission routes1
Has abstractyes

Explore more

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