Diversity Now! Sequins, Style & the End of Gender with Dr. Madison Moore
Bibliographic record
Abstract
Diversity Now! is an annual lecture series hosted by the Centre for Fashion Diversity and Social Change at Ryerson University in Toronto, Canada. This lecture series explores how individuals have used fashion as a means to inspire social change and political advocacy in their personal lives, their community, or the fashion industry. The 2019 guest lecturer was Dr. Madison Moore, an artist-scholar, DJ, and Assistant Professor of Gender, Sexuality and Women’s Studies at Virginia Commonwealth University. In this seventh series lecture, Moore discussed the journey and research behind his recent book Fabulous: The Rise of The Beautiful Eccentric (2018). Drawing on autobiography, anecdotal evidence, and interviews, Moore took his audience on a journey from his childhood in Ferguson, Missouri, to the night scene in New York, London, and Berlin, with an emphasis on Vogue Balls and catwalks. While Moore’s lecture drew on various sources, his message was unequivocal: style and clothes have the power to inspire social change.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.039 | 0.009 |
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".