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Record W7105894572 · doi:10.1139/facets-2025-0126

Canadian physics counts: an exploration of the diverse identities of physics students and professionals in Canada

2025· article· en· W7105894572 on OpenAlexafffundvenueabout

Bibliographic record

VenueFACETS · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicCareer Development and Diversity
Canadian institutionsDalhousie UniversityUniversity of TorontoWilfrid Laurier University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsRepresentation (politics)Diversity (politics)Inclusion (mineral)IndigenousIntersectionalityWhite (mutation)Gender balanceEthnic group

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.082
Threshold uncertainty score0.254

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.050
GPT teacher head0.319
Teacher spread0.269 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2025
Admission routes4
Has abstractyes

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