Perceived Occupational Gender Composition: A Census and Exploration
Bibliographic record
Abstract
Abstract Purpose Answering two questions: What do people believe is the gender makeup of different occupations? If there is a systematic difference between the actual and perceived gender composition what factors predict or mediate this difference? Methodology/Approach We integrate three occupation-level datasets: ratings of perceived gender composition and cultural sentiments (EPA ratings) for every 2010 Census occupation collected for this study, occupational characteristics from O*NET, and demographic characteristics from the 2015 to 2019 Current Population Survey. Regression models examine the association between sentiments and objective occupational traits on the perceived gender composition net of the actual gender composition. Findings While respondents underestimate extreme values, perceptions largely reflect actual composition. Gendered sentiments had a significant independent effect on gender composition perceptions. Examining the relationship between objective occupational features, sentiments, and perceptions allows scholars to better understand the links between structural conditions, gendered beliefs, and social action. If individuals underestimate the extent of gender segregation and view some occupations as more diverse than they are, they may be more willing to consider occupations inconsistent with their gender identity. On the other hand, if they misperceive gender composition because of cultural sentiments, they may choose an occupational course somewhat different from their intentions. Originality/Value of the Chapter Research on gender composition typically employs either a macro approach based on governmental statistics or a micro approach that examines a limited number of occupations. This is the first study to conduct a complete census of every Census occupation for perceived gender composition and cultural sentiments.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".