The role of the textile materials library: Providing access to multimodal knowledge in design research
Bibliographic record
Abstract
This article explores the role of the physical and virtual textile materials library as an effective multimodal learning and research tool in its provision of tactile, visual samples, technical data and project application information. The abundance of new materials and their increasing complexity is causing designers to rethink the traditional 'samples in a box' approach. The research uses a case study approach that includes consultation with industry, academia, and a review of four contemporary material libraries to explore the formats that future textile libraries may best take. In considering a future textile library, the authors also examine answers to the question: as the number of textiles available continues to expand, how can physical material, image and text-based library techniques best be combined with the web and digital information to make it possible for colleges, public institutions and companies to keep a textile library updated.
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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 | 0.027 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.008 | 0.015 |
| Scholarly communication | 0.024 | 0.031 |
| Open science | 0.002 | 0.021 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.019 | 0.004 |
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".