Evidence for “Pushed Out” and “Opt Out” Factors in Women’s Career Inclusion Across the World of Work in the United States
Bibliographic record
Abstract
There is an ongoing debate over the extent to which women “opt out” and/or are “pushed out” of various occupations ( Kossek et al., 2017 ). To advance this debate, we explore the correspondence of women’s interests in stereotypically masculine work activities with the work activities of their occupations/occupational-aspirations. We examine 42,631 responses to a survey of employed and unemployed persons in the United States and analyze associations along all six of Holland’s ( 1997 ) interest/work-activity dimensions. Overall, we find support for a “pushed out” perspective as women’s interests in hands-on/practical, analytic/scientific, and managerial/sales-related work activities are less strongly associated with being employed in occupations with those activities – in comparison to similarly interested men. However, these effect sizes are small and we find support for “opt out” dynamics in relation to hands-on/practical occupations. Altogether, our results indicate the need to continue looking beyond women’s vocational interests as explanations of their underrepresentation.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".