Exploring the Gender Gap in Engineering: A Re‐Specification and Test of the Hypothesis of Cumulative Advantages and Disadvantages
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Researchers using the hypothesis of cumulative advantages and disadvantages argue that the accumulation of small advantages for men and small disadvantages for women contributes to the gender gap in engineering. This paper uses data from a 1998 survey of engineering undergraduates to test a re‐specification of this hypothesis that treats the gender distribution of advantages and disadvantages as an empirical question. We considered four sets of factors that have been shown to promote choice of an engineering major, persistence in engineering, and progress in engineering: family background, high school participation in mathematics and science, university participation in engineering, and integration into engineering. We found gender differences for each set of factors. We also found that men and women accumulate different advantages and disadvantages as they move through the education pipeline. By demonstrating that the accumulation of advantages and disadvantages is gendered, these results highlight the importance of examining the impact of micro‐inequities on the persistence and progress of men and women in engineering.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it