Bibliographic record
Abstract
The Boston Museum of Fine Arts’ recent 2019 exhibition, Gender Bending Fashion, explored some of the ways in which designers and wearers in European and American contexts have challenged traditional ideas around dress and gender over the last century. This included the rejection of conventional dress codes (in the form of men wearing skirts and women wearing suits); the blurring of gender lines in fashion (the combination of “masculine” and “feminine” design elements and the construction of unisex clothing); as well as attempts to transcend the idea of gendered dress altogether (through the creation of new forms of genderless clothing). This review highlights key objects featured in the exhibition, with special attention paid to everyday ensembles and personal narratives that effectively communicated ideas of embodiment, cultural experience, and fashion storytelling that were missing from some of the high fashion garments on display. The deliberately critical and academic approach taken by the curatorial team is discussed, as are some of the tensions and material challenges inherent in representing different bodies and expressions of gender in the context of a major museum fashion exhibition. This exhibition addresses themes that are of critical importance to fashion curators, scholars, and anyone interested in fashion studies more generally.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".