Maximizing Women's Motivation in Domains Dominated by Men: Personally Known Versus Famous Role Models
Bibliographic record
Abstract
Two studies ( n = 1,522) examined the impact of role models in sport and science, technology, engineering, and mathematics (STEM) domains where gender discrimination has resulted in a lack of high-profile women. We examined the role of gender matching of personally known and famous exemplars on women's and men's motivation. Participants nominated a woman or man in sport (Study 1) or STEM (Study 2) who was either famous or known to them personally; they then indicated the extent to which they perceived this individual to be a motivating role model. Women and men were more motivated by personally known (vs. famous) role models. For famous exemplars, both women and men were most motivated by same-gender models (Studies 1 and 2). For personally known exemplars, men were similarly motivated by same- and other-gender models (Studies 1 and 2), but women were more motivated by same-gender models in sport (Study 1). Mediation analyses indicated that personally known (vs. famous) exemplars and, for women, same- (vs. other-) gender exemplars, were perceived as more attainable future selves and consequently were more motivating (Study 2). Given that there are fewer famous women in domains dominated by men, it is important to know if women can be inspired by personally known rather than famous individuals. These studies provide insight into the kinds of exemplars that are most motivating for women and may serve as a guide for educators and other practitioners seeking to provide the best role models for girls and women in domains dominated by men. Additional online materials for this article are available on PWQ's website at http://journals.sagepub.com/doi/suppl/10.1177/03616843231156165 .
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 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".