Bibliographic record
Abstract
Overall, Glasbeek concludes that maternal feminists’ belief that moral typologies could be mapped onto criminal law enforcement with relative ease resulted in their strong support for the Toronto Women’s Court.Overall, this study combines an excellent use of archival and primary sources with a sophisticated and nuanced assessment of the women associated with the Toronto Women’s Court, including both its proponents and supporters as well as those who appeared there as accused persons. Glasbeek is particularly careful in assessing the “evidence,” noting how often the circumstances and views of accused women are presented by official reports and commentaries, not by these women themselves, many of whom were “poor, sometimes homeless, often illiterate, and relatively powerless.” Her meticulous review of archival sources includes city jail registers at three-year intervals, official crime statistics, press reports (particularly the police court columns), and case files of the Andrew Mercer Ontario Reformatory for Females, where many women were imprisoned with indeterminate sentences and then later released subject to parole supervision by the Mercer. Particularly in Chapters 4 and 5, but also throughout the book, Glasbeek’s review of women’s “cases” provide important data about women and criminality in Toronto in the early decades of the twentieth century, even though they may well reveal only a fraction of their stories. As Glasbeek suggests, some of these women’s stories provide a “counterpoint” to the interpretations routinely offered by the TLCW in their public pronouncements about social problems relating to women and crime, and the need for a Women’s Court to dispense equal justice.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.033 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 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".