“A Recipe for Disaster?”: Female-Breadwinner Relationships Threaten Heterosexual Scripts
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".