Some pioneering Canadian women chemists: lives and contributions
Bibliographic record
Abstract
During Dr. Margaret-Ann Armour’s career, she was always passionate about the need to encourage more young women to enter chemistry and chemistry-related careers. One way of doing this is by means of role models, specifically to share stories of previous generations of Canadian women chemists. In fact, it is necessary to make them aware that there were Canadian women chemists, even at the beginning of the 20th century. Yet, no unitary source of information on Canadian women chemists has existed. This contribution will provide six selected life stories to partially remedy this deficiency and to offer those who wish to inspire future generations of women chemists with names and stories from the past. The individuals included here are Maude Menten, Clara Benson, Mary Dover, Bella Marcuse, Carol Edna Robertson (Maass), and Norah Vernon Barry (Toole).
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.071 | 0.027 |
| Scholarly communication | 0.012 | 0.004 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".