Glamour in the Good: Exhibiting Glamour's Expression in Toronto's Urban Imaginary
Bibliographic record
Abstract
This dissertation examines the beguiling but illusory concept of glamour as a historical and aesthetic component of Torontos urban imaginary. In the years since William Howland, Torontos 25th mayor from 1886-87, coined the phrase Toronto the Good, Toronto has undeniably shed its good moniker and steadily positioned itself as a glamorous world-class city. This dissertation uses glamour as a prism to illustrate how the citys literature, art, fashion, music and film have contributed to Torontos emergence as a cosmopolitan global city. Alongside these art forms, this study uses a comparative analysis to examine how historical and cultural developments such as the construction of Casa Loma, the popularity of the 1926 Miss Toronto pageant, the 1960s counterculture of Yorkville, the celebrity of the Toronto International Film Festival, the aftermath of the 1990s recession and the rise in gentrification have helped to shape a unique expression of glamour for the city. \nDrawing upon archival materials, primary and secondary sources, and close readings, this dissertation charts a history of glamour in Toronto and demonstrates how Torontonians have continuously negotiated, courted, and challenged the expression of glamour in the city. This history reveals the role gender and sexual orientation have played in the rise of glamour in Toronto and the conditions that shape the way women, in particular, have accessed and experienced glamour in the city. Ultimately, these contexts shed light on the ways in which Torontonians have historically navigated the tensions between glamour and the good.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.026 | 0.036 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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".