Bibliographic record
Abstract
Nataley Nagy and Frances Dorsey deserve our very special thanks for the organization of such a splendid symposium, which indeed exceeded our expectations and goals. Thanks are also due to the dedicated staff of the Textile Museum of Canada, of which Nataley is Director, and to the staff at Harbourfront Centre, and its Director, Melanie Egan. We also thank Debbie Adams of Adams + Associates Design Consultants, Inc., who served as artistic director for the Symposium; her designs, logo and artwork, have served us as well for the Proceedings. Ann Svenson Perlman stepped in to take on the role of co-editor, and she has done an outstanding job in formatting, sizing, and placing images, and in sharing editorial responsibilities. For proof-reading and editorial suggestions, we also acknowledge the contributions of Pat Hickman, Vice President; Pam Parmal, Past President, Sumru Krody, chair, TSA Publications Committee; Janice Lessman-Moss, External Relations Director; Lisa Kriner, Internal Relations Director; Karen Searle, Newsletter Editor; Kim Righi, Executive Director. For general guidance, the Executive Committee and the Board of Directors of the Textile Society of America have offered a constant source of much appreciated support. Final thanks, which are really first and foremost, are to our speakers and presenters, now authors, without whose enthusiasm, dedication, commitment, spirit of inquiry and integrity, we could not have the published papers to stimulate our thoughts for the future.
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 | 0.008 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.433 | 0.299 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".