HIGH SCHOOL CHOICES AND THE GENDER GAP IN <scp>STEM</scp>
Bibliographic record
Abstract
Women are less likely than men to graduate with a degree in science, technology, engineering, or math (STEM). We use detailed administrative data for a recent cohort of Ontario high school students, combined with data from the province's university admission system, to analyze the sources of this gap. We show that entry to STEM programs is mediated through an index of STEM readiness that depends on end‐of‐high school courses in math and science. Most of the gender gap in STEM entry can be traced to differences in the share of college entrants who are STEM‐ready; only a small share is attributable to differences in the choice of major conditional on readiness. We then use high school course data to decompose the gender gap in STEM readiness into two channels: one reflecting the gap in the fraction of high school students with the necessary prerequisites to enter STEM, the other arising from differences in the overall fractions of females and males who enter university. The gender gap in the fraction of males and females with STEM prerequisites is small. The primary driver of the gap in STEM readiness is the low rate of university entry by nonscience‐oriented males. (JEL I21, 28, I20)
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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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.012 | 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".