Concealable Stigma and Occupational Segregation
Bibliographic record
Abstract
Numerous scholars have noted the disproportionately high number of gay and lesbian workers in certain occupations, but systematic explanations for this type of occupational segregation remain elusive. Drawing on the literatures on concealable stigma and stigma management, we develop a theoretical framework predicting that gay men and lesbians will concentrate in occupations that provide a high degree of task independence or require a high level of social perceptiveness, or both. Using several distinct measures of sexual orientation, and controlling for potential confounds, such as education, urban location, and regional and demographic differences, we find support for these predictions across two nationally representative surveys in the United States for the period 2008–2010. Gay men are more likely to be in female-majority occupations than are heterosexual men, and lesbians are more represented in male-majority occupations than are heterosexual women, but even after accounting for this tendency, common to both gay men and lesbians is a propensity to concentrate in occupations that provide task independence or require social perceptiveness, or both. This study offers a theory of occupational segregation on the basis of minority sexual orientation and holds implications for the literatures on stigma, occupations, and labor markets.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".