Bibliographic record
Abstract
The gender gap in science and mathematics remains an issue in Canada today, with far-reaching socioeconomic implications (Dionne-Simart et al., 2016). The gender gap is wider in physics than in most other science disciplines (Xu et al., 2015). In this qualitative study, I examine educator perspectives on the low retention rate of young women in physics. The experience and perceptions of female students and educators in physics can inform best practices for physics education in order to retain women in physics. I interviewed two female physics educators: a high school physics teacher, and a graduate physics Teaching Assistant. From these interviews, four themes emerge. The first is that participants believe that the gender gap is caused by differing interests, which may be shaped by societal norms and stereotypes. The second is that participants feel that having strong STEM programming and positive female role models in schools would do the most to reduce the gap. The third theme is that group-based learning should be used to promote inclusivity of young women in physics classrooms. Finally, both participants feel that institutional initiatives geared specifically towards young women sometimes reinforce gender stereotypes, and that promotional initiatives should be designed to foster gender inclusivity, rather than emphasize gender differences.
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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 | 0.029 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.018 | 0.010 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".