Bibliographic record
Abstract
This article explores the relationships between fashion, glamour, celebrity, and Canadian literature, focusing specifically on Toronto, Canada. I argue for the value of “reading glamour” into Toronto’s literature by examining how glamour provides a socio-cultural insight into character and plot development and, moreover, elevates the character of the city itself. No doubt certain authors conjure up a glamorous cachet with their coterie of bohemian intellectual and literary salons but the writing itself rarely approaches the same level of glamorous celebration. However, reading glamour—that is, following Brown, tracing the language and grammar of glamour as a literary form linked to modern mass culture—extends the potential for literary and cultural expression of the text. As Gundle and Castelli argue, glamour is typically associated with the urban and cosmopolitan, and this paper explores how Toronto has historically engaged with its own sense of burgeoning celebrity, fashion, and glamour. By focusing on the work of Phyllis Brett Young’ s The Torontonians(1960), I examine how glamour as a corollary to fashion challenges preconceptions of “Toronto the Good,” not only within the local urban imaginary but also on national and global levels.
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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.020 | 0.017 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".