Bibliographic record
Abstract
<JATS1:p>The last decade has seen the growing popularity and visibility of fashion as a cultural product, including its growing presence in museum exhibitions. This book explores the history of fashion displays, highlighting the continuity of past and present curatorial practices. Comparing and contrasting exhibitions from different museums and decades—from the Paris Exposition Universelle of 1900 to the Alexander McQueen Savage Beauty show at the Metropolitan Museum of Art in 2011, and beyond—it makes connections between museum fashion and the wider fashion industry.</JATS1:p> <JATS1:p>By critically analyzing trends in fashion exhibition practice over the 20th and early 21st centuries, Julia Petrov defines and describes the varied representations of historical fashion within British and North American museum exhibitions. Rooted in extensive archival research on exhibitions by global leaders in the field—from the Victoria and Albert and the Bath Fashion Museum to the Brooklyn and the Royal Ontario Museums—the work reveals how fashion exhibitions have been shaped by the values and anxieties associated with fashion more generally.</JATS1:p> <JATS1:p>Supplemented by parallel critical approaches, including museological theory, historiography, body theory, material culture, and visual studies, Fashion History in the Museum demonstrates that in an increasingly corporate and mass-mediated world, fashion exhibitions must be analysed in a comparative and global context. Richly illustrated with 70 images, this book is essential reading for students and scholars of fashion history and museology, as well as curators, conservators, and exhibition designers.</JATS1:p>
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 | 0.002 | 0.004 |
| Science and technology studies | 0.007 | 0.012 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 0.002 |
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".