Why Are Most University Students Women? Evidence Based on Academic Performance, Study Habits and Parental Influences
Bibliographic record
Abstract
In this study, we use new Canadian data containing detailed information on standardized test scores, school marks, parental and peer influences, and other socio-economic background characteristics of boys and girls to try to account for the large gender gap in university attendance. Among 19-year-old youth in 2003, 38.8% of girls had attended university, compared with only 25.7% of boys. However, young men and women were about equally likely to attend college. We find that differences in observable characteristics between boys and girls account for more than three quarters (76.8%) of the gap in university participation. In order of importance, the main factors are differences in school marks at age 15, standardized test scores in reading at age 15, study habits, parental expectations and the university earnings premium relative to high school. Altogether, the four measures of academic abilities used in the study overall marks, performance on standardized reading tests, study habits and repeating grade collectively account for 58.9% of the gender gap in university participation. These results suggest that understanding why girls outperform boys in the classroom may be a key to understanding the gender divide in university participation.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".