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Record W4392511156 · doi:10.1111/pere.12539

How couples think about money: Types of money motives and relationship satisfaction

2024· article· en· W4392511156 on OpenAlexafffund
Johanna Peetz, M. Joseph

Bibliographic record

VenuePersonal Relationships · 2024
Typearticle
Languageen
FieldPsychology
TopicAttachment and Relationship Dynamics
Canadian institutionsCarleton University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsPrideRomancePsychologySocial psychologySample (material)Similarity (geometry)Impulse (physics)Political scienceLaw

Abstract

fetched live from OpenAlex

Abstract Two studies examined how financial values held by romantic partners were linked with relationship satisfaction. Across a sample of married individuals ( N = 628), and a dyadic sample ( N = 236), results suggest that holding or perceiving a romantic partner to hold integrated money motives—wanting to earn money to feel pride, establish one's worth, facilitate freedom, and enrich leisure activities—was linked with better relationship satisfaction. Holding or perceiving a romantic partner to hold nonintegrated money motives—wanting to earn money to enable impulse spending, to feel better than others, and to overcome self‐doubt—was linked with worse relationship satisfaction. In both samples, perceived similarity in money motives between the self and the partner was also linked to higher relationship satisfaction. Study 2 further showed that actual similarity between partners in nonintegrated money motives was also linked to better relationship satisfaction, suggesting that even nonintegrated money motives might benefit relationships, as long as both partners share these motives. Overall, these studies suggest that while holding similar financial values as your partner is linked with better relationships, some financial values are more conducive to relationship satisfaction than others.

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)0.000
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 score0.272
Threshold uncertainty score0.740

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0000.000

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 head0.050
GPT teacher head0.351
Teacher spread0.301 · 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

Citations6
Published2024
Admission routes2
Has abstractyes

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