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Record W2171546809 · doi:10.1177/1069397112450854

Valuing Romantic Relationships

2012· article· en· W2171546809 on OpenAlexaffabout
Geoff MacDonald, Tara C. Marshall, Judith Gere, Atsushi Shimotomai, July Lies

Bibliographic record

VenueCross-Cultural Research · 2012
Typearticle
Languageen
FieldPsychology
TopicEvolutionary Psychology and Human Behavior
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsRomancePsychologySocial psychologyValue (mathematics)Self-esteemSelection (genetic algorithm)Positive relationshipConstraint (computer-aided design)Developmental psychology

Abstract

fetched live from OpenAlex

Research has suggested that individuals lower in self-esteem restrain from fully valuing romantic relationships because of relatively low confidence in positive regard from their partners (i.e., positive reflected appraisals). MacDonald and Jessica (2006) provided evidence that in Indonesia, where family plays an important role in mate selection, low self-esteem also leads to doubts regarding family approval of the relationship that, in turn, places an additional constraint on fully valuing a romantic relationship. In the current research, Study 1 replicated these findings, showing that the positive relationship between self-esteem and value placed on a romantic relationship was mediated by both reflected appraisals and approval from a partner’s family in Indonesia but only reflected appraisals in Canada. In Study 2, the relationship between self-esteem and relationship value was mediated by reflected appraisals and approval from own, but not partner’s, family in Japan whereas only reflected appraisals played a mediating role in Australia. These data suggest that in cultures involving family in mate selection, placing full value on romantic relationships may be contingent on confidence in both reflected appraisals and family approval of the relationship.

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 categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.213
Threshold uncertainty score1.000

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 studies0.0020.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0080.019

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 head0.400
GPT teacher head0.563
Teacher spread0.163 · 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; both teacher heads agree on what is shown here.

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

Citations33
Published2012
Admission routes2
Has abstractyes

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