Squeezing In and Zipping Up: \nCanada’s Involvement in the late 20th and 21st Century Trend of Fashion Exhibition
Bibliographic record
Abstract
This thesis discusses the development of 20th century fashion within the space of Canadian museums. I argue that while Canada has the means to conceive groundbreaking exhibitions of 20th century dress, the absence of costume collections in Canadian fine art museums and the lack of collaboration between human history and fine art representatives have inhibited Canada’s contribution to the evolving multi-disciplinary trend of fashion exhibitions. By analyzing the evolution of dress history and exhibitions on an international scale, I study the differentiation offered by Valerie Stele between the antiquarian costume exhibit and the modern 20th century fashion exhibition largely influenced by the likes of Diana Vreeland and Cecile Beaton. After an analysis on the history of dress collection and exhibition in Canadian museums, I completed two case studies on Golden Age of Couture: Paris London 1947-1957 hosted at the Musée National des Beaux Arts du Québec and Elite Elegance: Couture Fashion in the 1950s conceived at the Royal Ontario Museum. A comparison is then made between these two exhibitions, coming from fine art and human history institutions respectively, between the realities of hosting and conceiving a dress exhibition, the reception of sponsorship, and the use of supplementary material.
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 | 0.016 | 0.009 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".