Gendered achievement gaps in introductory science courses.
Bibliographic record
Abstract
Although they represent the overall majority of students at Canadian universities studying in science fields, women make up only about 25% of STEM professionals (Simon et al., 2017). Several explanations for this pattern have been suggested, including gendered differences in such variables as societal attitudes toward suitable careers, effects of high stakes testing and achievement in undergraduate GPA (Lauer et al., 2013). Comparison of male vs female final course grades from introductory science courses at Western University over the last five years revealed a gender gap in achievement in some courses, but not others. Female students’ grades were consistently and significantly lower in introductory biology and physics courses relative to their male peers. The gendered achievement gap widened over time as a given course increased high-stakes testing. Gendered achievement gaps were not observed in a calculus course or a second year hands-on laboratory course.\nJoin us for a conversation around gender gaps in science education and what can we do to address these challenges.\nLauer, S., Momsen, J., Offerdahl, E., Kryjevskaia, M., Christensen, W., & Montplaisir, L. (2013). Stereotyped: Investigating gender in introductory science courses. CBE Life Sciences Education, 12(1), 30–38. https://doi.org/10.1187/cbe.12-08-0133\nSimon, R. M., Wagner, A., & Killion, B. (2017). Gender and choosing a STEM major in college: Femininity, masculinity, chilly climate, and occupational values. Journal of Research in Science Teaching, 54(3), 299–323. https://doi.org/10.1002/tea.21345
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".