VISUAL MAPPING STRATEGIES TO ORGANIZE, COMMUNICATE AND MAINTAIN DESIGN KNOWLEDGE
Bibliographic record
Abstract
Responding to increasingly demanding customers, facing global competition and managing the life cycle of a product are all instances of problematic situations that force different players in the fashion, textiles and clothing industry to be more creative in their search for solutions. It therefore becomes in some cases a priority to implement simple systems in order to ensure the availability of tools and information, so that teams can react quickly to problems that slow down product development. The goal was to improve and facilitate the processes of innovation and clothing conceptualization while having a more reliable performance and a greater capacity for adaptation during the creative processes. Various tools have been developed over the past few years, such as mind mapping and several visual mapping tools in order to organize, communicate and maintain knowledge regarding collection design in the fashion industry. It is a practice open to organizing ideas and collaborating on a specific topic, all of this through a graphic representation that is creative and adapted to the development of thought. This article describes the characteristics and relevance of the application of visual mapping tools in the context of product development from a study with fashion designers. The results of this project confirm that the heuristic approach in a design process could potentially facilitate the marketing of fashion clothing products.
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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".