Masculinity Attitudes Across Rural, Suburban, and Urban Areas in the United States
Bibliographic record
Abstract
This article uses the 2011–2019 National Survey of Family Growth to explore how masculinity attitudes differ by rural, suburban, and urban contexts across three social axes: sexual identity, race/ethnicity, and education. It examines within-group differences based on spatial context among 17,944 men aged 15–44 who are straight, gay/bisexual, Black, white, and Latino, as well as among men with less than a bachelor’s, a bachelor’s, and more than a bachelor’s. This contributes to existing knowledge in several ways: it is the first project to build on important qualitative studies through the use of a nationally representative sample; it contributes to the scarce research on how rural gay/bisexual, Black, and Latino men understand masculinity; and it examines how education shapes the relationship between spatial context and attitudes about masculinity. Results indicate that spatial context has a stronger relationship to attitudes among white men, straight men, and men without a bachelor’s than among Black men, Latino men, gay/bisexual men, or men with a bachelor’s or above. Theoretically, what this shows is that spatial context is more strongly related to masculinity attitudes for men who are advantaged on the basis of sexuality or race than for men who are marginalized on these axes. When significant differences emerged, rural men were more conservative than urban and suburban men, and suburban men were more conservative than urban men. These results show that there is a relationship between spatial contexts and attitudes about masculinity, but that it depends on social identity and level of education.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".