Review: Diversity Now! 2020 with Becca McCharen-Tran, CHROMAT
Bibliographic record
Abstract
For the eighth iteration of the Diversity Now! lecture series, the Research Centre for Fashion & Systemic Change invited Becca McCharen-Tran to Ryerson University to speak about her experiences founding the fashion label Chromat.Initially educated in architecture from the University of Virginia, McCharen-Tran has become a well-known vanguard for inclusivity in fashion, making her signature queer futuristic sportswear for sizes XS-4XL.She was awarded runner-up in the CFDA/Vogue fashion fund in 2017, made Forbes' 30 Under 30 list for "People Who are Reinventing the World in 2014," and her clothing has been worn by celebrities like Beyoncé and Madonna.In her talk, McCharen-Tran offered candid advice on establishing a label with inclusivity at the core of its DNA, the key take-away being that inclusivity requires work and the willingness to prioritize the needs of those most often excluded.
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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.085 | 0.044 |
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".