Textiles as a catalyst in the co-creative design process
Bibliographic record
Abstract
This paper presents findings relating to the crucial role of textiles in the Emotional Fit (Townsend et al. 2016) collaborative research project, which is investigating a person-centred, sustainable approach to fashion for an ageing demographic. Working with a group of Nottingham women (aged 55+) the team have accrued and responded to data drawn from in-depth interviews, wardrobe inventories and body measurements, to develop a collection of co-designed fashion prototypes that aim to meet the physical and emotional needs of their participants. By integrating geometric cutting with carefully selected and bespoke printed textiles, the resulting minimal waste garments enable wearers to express themselves via universal silhouettes incorporating multiple styling options, in support of personal agency and product longevity. \nTextiles act as the catalyst for the design and project development process by: providing a starting point for shape making through draping on the body and mannequin; as sensorial substrates to elicit tactile responses and interactions; as the surface for photographic imagery, engineered patterns and contrasting volumes, to be enacted by the human form. \nThe project demonstrates how such a co-creative or hacking approach necessitates a shift away from the established hierarchical fashion system (Busch 2009) that often undervalues its consumers. Here, by contrast, we actively explore the potential customer’s lived experience of the relationship between body, cloth and dress to inform a more holistic fashion design philosophy. The methodology challenges the generally accepted view of the textiles as subordinate to the practice of fashion, by documenting normally unspoken exchanges with the semantics of fabric through handling, manipulation, testing, printing, toiling and constructing. By reflecting on the aesthetics of cloth in relation to the emotional fit of clothing, we illustrate how it is intrinsic within the creative decision-making process, whereby embodied associations with the past point towards newly imagined wearable futures. \nby the Textiles programme at the University of Dundee between 2012–2014; collaborative design-led research projects that supported local medical and health care companies by providing key expertise in textile design, functional clothing design methodologies and user-centred processes for design-led innovation. Analysis and discussion focuses on understanding the challenges and benefits of collaborative research between academia and local enterprise to textile design innovation, local economy, society, and education.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".