The Under-Utilization of Women’s Talent: Academic Achievement and Future Leadership Positions
Bibliographic record
Abstract
Abstract Despite high labor force participation, women remain underrepresented in leadership at every level. In this study, we examine whether women and men who show early academic achievement during their adolescence—and arguably signs of future leadership potential—have similar or different pathways to later leadership positions in the workplace. We also examine how leadership patterns by gender and early academic achievement differ according to parenthood status. Using data from the National Longitudinal Survey of Youth 1979, we find that overall, men supervise more people than women at work during their early-to-mid careers, regardless of their grade point averages (GPAs) in high school. In addition, among men and women who are parents, early academic achievement is much more strongly associated with future leadership roles for fathers than it is for mothers. Such patterns exacerbate gender gaps in leadership among parents who were top achievers in high school. Indeed, among those who had earned a 4.0 GPA in high school, fathers manage over four times the number of supervisees as mothers do (nineteen vs. four supervisees). Additional analyses focusing on parents suggest that gender leadership gaps by GPA are not attributable to different propensities for taking on leadership roles between the genders but are in part explained by unequal returns to educational attainment and differences in employment-related characteristics by gender. Overall, our results reveal that suppressed leadership prospects apply to even women who show the most promise early-on and highlight the vast under-utilization of women’s (in particular mothers’) talent for organizational leadership.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".