Bibliographic record
Abstract
Tis book explores people's relationships to a signifcant place in Toronto, and how both that place and those relationships changed amid larger urban transformations.It uses historical evidence and methods to present a narrative of Yonge Street's remaking from the 1950s through the 1970s, while ofering explanations of why that process turned out as it did.My own experiences are not part of that story, but they have inevitably shaped how I've told it.Like most people who grew up in Toronto, I have my own memories of downtown Yonge.I've been a consumer and a teenage loiterer there; I've shopped, had nights out, marched in demonstrations, and been stuck in trafc while parades crawled by.For a few years as an undergraduate student, I spent evenings and weekends selling boots at the Eaton Centre.Individually, none of those experiences were particularly important.But together, by showing me the street's changes and diferent faces over time, they helped me see the possibilities of studying a place like Yonge.Researching and writing this book has kept me busy for almost a decade, and many people have helped and supported me along the way.I began the research for Te Heart of Toronto at York University, where I crossed paths with a fantastic bunch of historians.I'm particularly grateful to Marcel Martel and to Colin Coates for their constant guidance and friendship.Like my other mentors in York's history department, they modeled an egalitarian style of teaching and advising that continues to shape how I approach my job today.Marlene Shore, Craig Heron, Richard White, Harold Bérubé, and Roger Keil all provided valuable feedback on earlier versions of this project.Tanks also to my fellow grad students, who exposed me to new ways of thinking about history, while showing me the value of doing that work in a like-minded community.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.461 | 0.277 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".