A Timeline of Firsts for Women in Statistics in Canada
Bibliographic record
Abstract
Statistical sciences have seen enormous opportunity and growth over recent past decades. The evolution of statistical tools are not the only change. The numbers graduating with undergraduate degrees in mathematics and statistics has more than doubled in Canada since 2009, and the profession of Assistant Professors now celebrates a nearly even female-male gender split when pooled across all departments and Canadian universities. The latter is a big change from 1970/1971, the first year with data, when women accounted for barely 10% of Assistant Professors. Substantial change over time gives rise to questions about what has happened over the past decades. The poster overlaying timelines of major accolades by women in statistics in Canada with a brief history of the professional soceities and federal legislative changes. We are grateful for the support and feedback of individuals who provided feedback, names, and events: David Bellhouse, Judy-Anne Chapman, Charmaine Dean, Christian Genest, Nadia Ghazzali, Peter MacDonald, Shirley Mills, Bouchra Nasri, Nancy Reid, Mary Thompson, Rowan Thompson In addition, representatives from a variety of groups and committees provided additional information and feedback: Fields, NSERC, Statistical Society of Canada Women in Statistics Committee, Statistical Society of Canada's Committee on Equity, Diversity and Inclusion, University of Manitoba Sponsors who provided funding and/or in kind support:CANSSI, CANSSI Ontario, Carleton University, Statistical Society of Canada Notes on choice of Women: Even though using a binary definition of gender is out of date, more general diversity was an initial direction of interest, but the journalistic complexity was considerable. The project is largely is inspired by the following works: Billard, L. And Kafadar, K. (2015) “Women in Statistics: Scientific Contributions Versus Rewards“ in ‘Advancing Women in Science: An International Perspective’, W. Pearson, Jr., et al. (eds.), DOI 10.1007/978-3-319-08629-3_7 Reid, N. (2014) “The whole women thing”, in ‘Past, Present, and Future of Statistical Science’ X. Lin et al (eds.) DOI 10.1201/b16720Stinnett, S (1990) "Women in Statistics: Sesquicentennial Activities” The American Statistician, Vol 44, No 2, pp 74-80 Thomson, M (2014) “Reflections on Women in Statistics in Canada”, in ‘Past, Present, and Future of Statistical Science’ X. Lin et al (eds.) DOI 10.1201/b16720 Bellhouse, D. R., & Genest, C. (1999). A history of the statistical society of Canada: The formative years. Statistical Science (14). datasets: Statistics Canada. Table 37-10-0077-01 Number and median age of full-time teaching staff at Canadian universities, by highest earned degree, staff functions, rank, gender doi:10.25318/3710007701-eng Statistics Canada. Table 37-10-0235-01 Postsecondary graduates, by detailed field of study, institution, and program and student characteristics, doi:10.25318/3710023501-eng Français: https://doi.org/10.5281/zenodo.15733725
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.011 |
| Science and technology studies | 0.021 | 0.003 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.040 | 0.007 |
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".