Bibliographic record
Abstract
Christian Dior once said, “We invent nothing, we always start from something that has come before” (qtd. in Pochna 80). Historic garments can inform and inspire the present, offering up design potential for reinterpretations of styles of the past or serving as evidence of how fashion was worn and lived for material culture studies. Seeing a dress in a photo is a very different experience than feeling the weight of the fabric in hand, examining the details of cut, construction and embellishment, considering the relationship of the garment to the body or searching for evidence of how the garment was worn, used or altered over time. The Ryerson Fashion Research Collection is a repository of several thousand items acquired by donation since 1981, many of which are dresses and evening gowns dating from 1860 to 2000. For several years, this collection lay dormant behind an unmarked door and was largely unknown by the student body. This project was initiated to understand the nature of the artifacts contained therein and is a first step in the process of refocusing and rebuilding the Collection for the future. The title “Re-collection of the Ryerson Fashion Research Collection” encapsulates the organizing principle for this practice-led interdisciplinary project, encompassing the intersection of material culture, curatorial process and collective memory in the identification of one hundred key items from the archive that reflect the breadth and history of the Collection itself.
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 | 0.017 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.023 | 0.011 |
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".