Canadian physics counts: an exploration of the diverse identities of physics students and professionals in Canada
Bibliographic record
Abstract
The lack of diversity in physics remains a worldwide problem. Despite being a quantitative discipline relying on measurements, there is a paucity of data on the demographic characteristics of underrepresented groups. We present findings from Canadian Physics Counts: the first national survey of equity, diversity, and inclusion in the Canadian physics community. Our intersectional approach provides a wealth of information on gender identity, sexual orientation, race, disability, and more. Analyses revealed key findings, including the first comprehensive Canadian data on nonbinary or gender diverse physicists, and the first data on Black (1.2%) and Indigenous (0.3%) physicists, who were least represented. Assessing representation across roles and career stages, BIPOC (Black, Indigenous, and People of Colour), representation fell precipitously (by one-half) between under/graduate to professional roles, while White women’s representation remained relatively stable, and White men’s representation steadily increased. One in four BIPOC respondents from gender diverse backgrounds identified as disabled, and the proportion of sexually diverse students with disabilities was more than three times higher than the proportion of heterosexual students with disabilities, underscoring the necessity of intersectional analyses. Students were more demographically diverse than professionals, highlighting the importance of acting today to retain the diverse physicists of tomorrow
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.009 | 0.017 |
| Science and technology studies | 0.024 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".