Bibliographic record
Abstract
Women who graduate from university are less likely than men to specialize in science, technology, engineering, or math (STEM). We use detailed administrative data for a recent cohort of high school students in Ontario, Canada, combined with data from the province's university admission system to analyze the dynamic process leading to this gap. We show that entry to STEM programs is mediated through an index of STEM readiness based on end-of-highschool courses in math and science. Most of the gender gap in STEM entry can be traced to differences in the rate of STEM readiness; less than a fifth is due to differences in the choice of major conditional on readiness. We then use high school course data to decompose the gap in STEM readiness among university entrants into two channels: one reflecting the gender gap in the fraction of high school students with the necessary prerequisites to enter STEM, and a second arising from differences in the fractions of females and males who enter university. The gender gap in the fraction of students with STEM prerequisites is small. The main factor is the lower university entry rate by men -a difference that is due to the lower fraction of non-science oriented males who complete enough advanced level courses to qualify for university entry. We conclude that differences in course-taking patterns and preferences for STEM conditional on readiness contribute to male-female differences in the rate of entering STEM, but that the main source of the gap is the lower overall rate of university attendance by men.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".