Bibliographic record
Abstract
Award-winning author Merl Massie brings to the page the life and career of Sylvia Fedoruk (1927-2012), which encompassed some of the most ground-breaking scientific, athletic and public transformations of the twentieth century. A pioneer in leading-edge cancer research, primarily in the field of nuclear medicine, she was the first woman to join the Atomic Energy Board of Canada. Sylvia was an outstanding athlete, competing at an elite level in women’s softball and curling. Elected as the first woman chancellor of the University of Saskatchewan, she went on to be the first woman to serve as Lieutenant Governor of Saskatchewan, coaching two premiers through potential legislative and constitutional crises. With support from the University, the provincial government and the media, she withstood a major outing controversy, revealing a particular provincial touchpoint around issues of homosexuality, artistic activism, and power dynamics in the midst of the AIDS crisis of the 1990s. Known for a warm but no-nonsense style, Sylvia Fedoruk built a legacy which drew Saskatchewan’s north into the provincial consciousness, advocated for equal education for all, pushed for support for women in science, technology, engineering, and math, and worked tirelessly as the University of Saskatchewan and the province’s most vocal cheerleader.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.167 | 0.090 |
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".