Beyond gender: the intersectional impact of community demographics on the continuation rates of male and female students into high school physics
Bibliographic record
Abstract
This study examines the complex interplay of gender and other demographics on continuation rates in high school physics. Using a diverse dataset that combines demographics from the Canadian Census and eleven years of gendered enrolment data from the Ontario Ministry of Education, we track student cohorts as they transition from mandatory science to elective physics courses. We then employ hierarchical linear modelling to quantify the interaction effects between gender and other demographics, providing a detailed perspective on the on continuation in physics. Our results indicate the racial demographics of a school’s neighbourhood have a limited impact on continuation once controlling for other factors such as socioeconomic status, though neighbourhoods with a higher Black population were a notable exception, consistently exhibiting significantly lower continuation rates for both male and female students. A potential role model effect related to parental education was also found as the proportion of parents with science, technology, engineering, and mathematics (STEM) degrees correlates positively with increased continuation rates, whereas an increase in non-STEM degrees corresponds with a reduced student continuation rate. The most pronounced effects are school-level factors. Continuation rates in physics are very strongly correlated with continuation in chemistry or calculus — effects that much stronger for male than female students. Conversely, continuation in biology positively correlates with the continuation of female students in physics, with little to no effect found for male students. Nevertheless, the effect sizes observed for chemistry and calculus markedly outweigh that for biology. This is further evidence that considering STEM as a homogeneous subject when examining gender disparities is misguided. These insights can guide future education policies and initiatives to increase continuation rates and foster greater gender equity and inclusivity in physics education.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".