Creating Complicated Lives: Women and Science at English-Canadian Universities, 1880-1980
Bibliographic record
Abstract
Why have Canadian women scientists been written out of the historical record? Who were they? What did they accomplish? What were their life paths? These are some of the questions answered in this authoritative work. Over decades of research, Marianne Ainley identified, tracked down, and interviewed surviving scientists. Creating Complicated Lives weaves the lives and work of these pioneers with the author's own experiences as an immigrant scientific technician and later a feminist historian. Ainley argues that we must look at the lives of women scientists through a new historical lens that takes into account both the advances of science and concurrent debates about the advancement of women. Rather than having linear career trajectories, many women shifted fields, coped with discrimination, and endeavoured to find niches in which they could make significant contributions. Never before has there been a survey of the lives and work of early Canadian women scientists. This nuanced study brings their stories to light, comparing, contrasting, and interpreting their very complicated lives.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.058 | 0.024 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".