Masculinity Attitudes Across Rural, Suburban, and Urban Areas in the United States
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it