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Record W4380232451 · doi:10.1515/9780228013273

Sweet Canadian Girls Abroad

2022· book· en· W4380232451 on OpenAlexaboutno aff
Cecilia Morgan

Bibliographic record

VenueMcGill-Queen's University Press eBooks · 2022
Typebook
Languageen
FieldSocial Sciences
TopicCanadian Identity and History
Canadian institutionsnot available
Fundersnot available
KeywordsPolitical science

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.064
Threshold uncertainty score0.201

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0330.004
Scholarly communication0.0060.002
Open science0.0010.004
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0530.005

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.

Opus teacher head0.012
GPT teacher head0.207
Teacher spread0.195 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations0
Published2022
Admission routes1
Has abstractyes

Explore more

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