Bibliographic record
Abstract
Fashion Studies is a new voice in our field. It will be published annually by Toronto Metropolitan University’s Centre for Fashion & Systemic Change and is an open access, academic journal in fashion studies, available to all at no cost to readers or authors. We join the movement of scholars who wish to push back against the exploitative corporate publishing monopoly that profits from publicly funded and free academic and creative labour. We believe in the democratic dissemination of scholarly and creative work that removes Western and elitist barriers and paywalls. Acceptance is based on a rigorous, double-blind peer review process by top scholarly and creative academics. One of the strengths of fashion studies is its interdisciplinarity. We welcome innovative work engaging with the study of fashion from all disciplines, including social sciences, humanities, and creative fields. We are open to diverse theoretical and methodological approaches in a broad range of written and creative formats. In light of the fact that fashion is one of the most exploitative industries of both people and the environment, we particularly encourage work that engages with the relationship between fashion, diversity, and social change historically and today. It is our aim to produce and disseminate work that both critiques the system and seeks to reimagine it, refashioning the world into a more equitable, just, and inclusive place.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.257 | 0.072 |
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".