Bibliographic record
Abstract
This paper outlines an ongoing project exploring the potential for knitted textiles to create architectural forms. The project is inspired by the processes of the engineer Heinz Isler who used textiles as a form-finding medium and subsequently set those forms into permanent structures, using the knowledge acquired during that process to create large scale forms inspired by the maquettes. This project examines how this approach could be explored using a textile design route and incorporating knowledge of the textile design process as both a technical and aesthetic act. The first part uses a collaborative workshop to transform tubular-knitted fabric into small and large sculptural models. These are then recontextualised with a focus on photographic output to present the outcomes as architectural forms. Second, thermoplastic polyurethane yarn (TPUY) is used to create knitted structures that exploit the innate potentials of knitted fabrics when used with heating methods to find forms to create architectural maquettes. These knitted structures are created on electronic Stoll knitting machinery and rely on tacit knit knowledge to create structures that capitalise on the adaptability of the knitting process. This reduces the need for seams or other areas that may cause weakness and allows the creation of both flat and three-dimensional shapes. Using various techniques, concentrations of TPUY and conventional knitting fibres, the project proposes an exciting future application for knitted textiles in the process of designing structures with potential in architecture, engineering and sculpture. Finally, the discussion moves to further potentials for this process in both research and teaching scenarios.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".