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Record W4407754818 · doi:10.1007/s11199-025-01560-y

“A Recipe for Disaster?”: Female-Breadwinner Relationships Threaten Heterosexual Scripts

2025· article· en· W4407754818 on OpenAlexafffund
Alexandra N. Fisher, Danu Anthony Stinson, Anastasija Kalajdzic, Hannah E. Dupuis, Erin E. Lowey, Elysia Desgrosseilliers, Annie MacIntosh

Bibliographic record

VenueSex Roles · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicGender Roles and Identity Studies
Canadian institutionsUniversity of Victoria
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsRecipePsychologyScripting languageDevelopmental psychologySocial psychologyGeography

Abstract

fetched live from OpenAlex

Abstract Female breadwinner relationships (FBRs) occur when a woman earns more money than her male romantic partner. In four studies, we used diverse methods to document the threat that FBRs pose to heterosexual scripts (i.e., social conventions for heterosexual romance). First, a reflexive thematic analysis of 94 newspaper and magazine articles about FBRs identified themes concerning social stigma and feelings of gender threat (i.e., co-occurring feelings of gender nonconformity and inadequacy) that undermine well-being for FBRs, alongside themes concerning hope for a more egalitarian future. Next, two pre-registered experiments ( Ns = 880 and 1612) revealed stigmatizing attitudes towards FBRs, which were perceived to be less desirable, worse quality, and less stable than male-breadwinner relationships. Finally, a cross-sectional study of married women and men ( N = 511) affirmed that feelings of gender threat partially explained FBRs’ poor relationship outcomes. Across all four studies, and consistent with theories of fragile masculinity, men suffered worse gender threat than women in FBRs. These findings offer novel insight into heterosexual scripts and the punishing social consequences for people who violate those scripts and suggest that social stigma about FBRs may pose a barrier to gender equality in close relationships and in society.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.466
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.082
GPT teacher head0.356
Teacher spread0.275 · 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

Citations4
Published2025
Admission routes2
Has abstractyes

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