Bibliographic record
Abstract
Breadwinning Daughters is about young working women who lived through one of the most challenging decades of the twentieth century, the Great Depression.It began as a doctoral thesis at the University of Toronto and was inspired by my desire to learn more about my grandmother, a young woman who left high school in the 1930s to look after her ill and unemployed father.It became a book because more than eighty women and men welcomed me into their homes and shared their memories.It is their generosity of spirit that made this study possible.For this, I thank them first and foremost.As I worked on my thesis and later my manuscript, I received help from many people.The skilled archivists and reference librarians at the Archives of Ontario, City of Toronto Archives, Thomas Fisher Rare Book Library, and the Government Document collection, Robarts Library, helped me tackle the documentary record.This study is much richer because of the impressive and valuable collections at the Multicultural History Society of Ontario.Curator Pasang Thackchooe offered important assistance in my efforts to sort through hundreds of interviews.Enrico Cumbo generously shared recordings from his own research, which broadened my source base in important ways.June Elliott and Ian Hamilton carefully read and edited the manuscript.Dick Duffin spent hours perfecting the images for the book.At the University of Toronto Press, I would like to thank my editor Len Husband for his support
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.236 | 0.113 |
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".