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Record W2488797884 · doi:10.1111/fare.12195

Qualities of Character That Predict Marital Well‐Being

2016· article· en· W2488797884 on OpenAlexaff
H. Wallace Goddard, Jonathan R. Olson, Adam M. Galovan, David G. Schramm, James P. Marshall

Bibliographic record

VenueFamily Relations · 2016
Typearticle
Languageen
FieldPsychology
TopicForgiveness and Related Behaviors
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsKindnessHumilityPsychologyGenerositySocial psychologyForgivenessCompassionCharacter (mathematics)PerceptionProsocial behaviorInterpersonal relationshipDevelopmental psychologyClinical psychology

Abstract

fetched live from OpenAlex

A growing body of literature has examined relations among qualities of character—or marital virtues—and marital outcomes. Results of past research have suggested positive relations between qualities such as generosity, kindness, and forgiveness, and marital well‐being. We expand on previous research by examining relations between three qualities of character and marital satisfaction with 1,513 respondents randomly selected from three states. Specifically, we examined the effects of participants' perceptions of their partners' humility, compassion, and positivity on their own marital satisfaction. Results indicated statistically significant, positive associations between each of these qualities and marital satisfaction, although results vary by gender. Furthermore, a statistically significant interaction effect suggested that spousal humility may be a protective factor against marital stress among women. Implications for practice and program development are discussed.

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)0.001
metaresearch head score (Gemma)0.004
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 score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
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 head0.030
GPT teacher head0.289
Teacher spread0.260 · 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

Citations31
Published2016
Admission routes1
Has abstractyes

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