Why Still so Few? A Theoretical Model of the Role of Benevolent Sexism and Career Support in the Continued Underrepresentation of Women in Leadership Positions
Bibliographic record
Abstract
We advance our understanding of women’s continued underrepresentation in leadership positions by highlighting the subtle, but damaging, role benevolent sexism, a covert and socially accepted form of sexism, plays in this process. Drawing on and integrating previously disparate literatures on benevolent sexism and social support, we develop a new theoretical model in which benevolent sexism of both women and those in their social networks (i.e., managers and intimate partners) affect women’s acquisition of career social support for advancement at two levels, interpersonal and intrapersonal, and across multiple domains, work and family. At the interpersonal level, we suggest that managers’ and intimate partners’ benevolent sexism may undermine their provision of the needed career support to advance in leadership positions for women. At the intrapersonal level, we suggest that women’s personal endorsement of benevolent sexism may undermine their ability to recognize and willingness to seek out career support from their family members (i.e., intimate partners) and managers for advancement to leadership positions. Implications for theory and future research are discussed.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".