Marital Status, Gender, and Race in The U.S.: Perceptions of Middle-Aged Men and Women
Bibliographic record
Abstract
A handful of studies conducted in the 1980s and 1990s find that undergraduate students perceive unmarried people less favorably than married people. The present research describes two experimental studies that revisit and extend this work by examining the extent to which perceptions of singles depend on marital history, gender, and race, both of which employ a more diverse sample of Americans via Amazon’s Mechanical Turk (MTurk). Black Americans are less likely to marry, more likely to divorce, and less likely to remarry than their White counterparts; Black women are less likely to marry than Black men; and Black women contend with nuanced stereotypes that portray them as strong, independent, and self-sufficient. These differences suggest race may shape beliefs about singles, and that racialized differences may be gendered. In Study 1, respondents rated a married or never married man or woman across a range of characteristics. In Study 2, respondents rated a White man, White woman, Black man, or Black woman who was either married, never married, or divorced. Across both studies, regression models indicate singles were evaluated more negatively than married people. Moreover, divorced Black women were perceived more positively on several measures compared to divorced members of other groups. For the most part, however, the magnitude of the singlism effect did not vary by marital history (never married or divorced), gender, or race. We note that null findings regarding gender and race are often relegated to the file drawer, but that this practice distorts the results of systematic reviews and perpetuates the misconception that groups of people (e.g., men and women, Blacks and Whites) are vastly different from one another, a belief that undergirds and justifies inequality.
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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 | 0.003 |
| 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 | 0.001 |
| 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".