Inspired and Inspiring Textile Designers
Bibliographic record
Abstract
This paper reports a study that investigated the synergies of influence (that which shapes the context for creative ideas) and inspiration (that which inspires the content) during the creation process of ten seasoned textile designers towards a better understanding of creativity. The behaviors and rhythm of textile designers were mapped using participant observation with multiple methods including field observation, capturing visual records using video equipment and photography, note taking, auditory records using mp3 recorders, and conducting informal dialogue during studio sessions. The results provide detailed interpretive information in the form of themes focused on understanding how they used and transformed inspirational sources towards the completion of their projects and how designers use their personal thesauruses through their sociocultural capital as influences. No two designers used and transformed inspirational sources in exactly the same way, yet each designer exhibited multiple methods of transformation by including an element of inspiration in their design process. Four separate themes emerged when we looked at how the designers used their personal thesauruses as influences. These themes were: (a) connections made to their sociocultural capital and through research, (b) narratives found or told to focus their projects, (c) emotions related to their attachments and reactions, and (d) design elements they referenced that aided in their decision making. Our rich descriptions of when and how often influence and inspirational sources play into the creative and problem-solving processes are a step toward a better understanding of creativity as a dynamic, complex, and diverse endeavor.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".