Entrepreneurship in the Fashion Industry
Bibliographic record
Abstract
In this chapter, we explore entrepreneurship in the slow fashion industry at a time of \nsignificant restructuring in the global fashion industry. Drawing on a case study of selfemployed \ndesigners in the slow fashion industry in Geneva (Switzerland), Rotterdam \n(The Netherlands) and Toronto (Canada), we argue that small, slow fashion businesses, \nthrough their innovative design, branding and retail practices, have carved out a unique \nniche in the hyper competitive fashion marketplace. In particular, we demonstrate that \nthey have a very innovative approach to sustainability, characterized by an interest \ntoward the use of salvage materials, the revalorization of craftsmanship, as well as a \ntendency for handmade productions. Driven by personal beliefs and values, these \ndesigners seem to wish to reconcile personal fulfillment with professional achievement \nas they seek to compete with the paradigm of fast fashion that continues to dominate the \nfashion industry. This paper contributes to our understanding of the entrepreneurial \npractices of emerging designers, in particular in the slow fashion industry. It also \ncontributes to the emerging studies in fashion and design-oriented industries that \nconsider the value craftsmanship and the wish to “stay local”, predicting a rise or return \nof the makers and small-scale manufacturing in contemporary cities.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".