Focusing Efforts to Improve Gender Equity in Physics Education: A Pan-Canadian Physics Education Research Network
Bibliographic record
Abstract
Women participating in physics play an essential role in Canada’s economic and research future through their potential to maximize research excellence and broaden horizons with diverse perspectives. However, the underrepresentation of women in physics remains a pressing concern for Canada, especially in higher education since the culture of inequity in the field impedes both its own and women’s progress. Research suggests a need for further physics education research and networking to increase the number of women in physics; the degree of collaborations between researchers in physics education is less than in other science fields due to fragmented connections. Currently, there is limited knowledge of (a) the status of physics education research (PER) across Canada (especially relating to gender), (b) Canadian universities’ stance on PER and its mobilization, and (c) how researchers, policy makers, and practitioners are working together in a network to improve the underrepresentation of women in physics. This proposed study aims to fill these knowledge gaps and investigate what capacity a pan-Canadian physics education research network may have to develop a culture of gender equity in the field.
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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 | 0.046 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.028 | 0.005 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 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".