Bibliographic record
Abstract
By the late nineteenth century, Canadian women had begun forging careers as professional actresses, appearing not just in Canada, but in the United States, Britain, Australia, and New Zealand. They played an integral role in theatrical networks and helped shape transnational middle-class culture. Taking the approach of feminist collective biography, Sweet Canadian Girls Abroad writes the lives of women who, despite their renown during their lifetimes, have been all too easily forgotten. Cecilia Morgan examines these “sweet girls’” childhoods, their experiences of work, touring, and company management, the plays in which they appeared, and the celebrity they enjoyed. In so doing she shows how women helped convey messages about race, empire, and white identity in popular culture. Investigating a period from the 1870s to the 1940s, Morgan demonstrates how actresses evolved within a period of change in theatre, how they coped with new challenges, and how they brought their craft to new media. Paying particular attention to the careers of Margaret Bannerman, Tony Award-winner Beatrice Lillie, Margaret Anglin, Julia Arthur, and Frances Doble, among many others, this book explores how being an actress abroad became work as well as profession for Canadian women. Extensively researched and generously illustrated, Sweet Canadian Girls Abroad argues for the importance of theatre, both to Canadian women’s history and to our understanding of Canada in a transnational world.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.033 | 0.004 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.053 | 0.005 |
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".