Refashioning the industry: Exploring the transition of women’s apparel brands to the circular economy in Montreal
Bibliographic record
Abstract
Over the last few decades, the fashion industry has evolved from providing well-conceived haute couture collections to the production of trendy, mass-produced, low-priced apparel, with reduced lead times. This consumption of ‘throwaway fashion’, at an alarming rate, has yielded extensive environmental and social detriments, thereby warranting industry reform and accountability. Driven by the tenets of sustainable development, brands within the fashion industry have since advanced to incorporate values that balance profits, people, and planet. As the circular economy gains traction, globally, to tackle sustainability challenges across industries, this research conducts a case study to explore the implementation of circularity strategies within the sustainable fashion industry in Montreal. Guided by Québec Circulaire’s Circular Economy Framework, practices employed by 90 sustainable apparel brands in Montreal, are identified and analysed to ascertain alignment with different circularity orientations and strategies. We found that a large number of sustainable brands in Montreal adopt practices that orient towards rethinking the extraction and use of virgin resources rather than optimising the use of extracted resources. They do so by employing a mix of strategies including eco-design, responsible procurement and consumption, and operations improvement. Further in-depth interviews conducted with 12 brands also revealed contextual factors that influence the transition of the industry to a CE model, including local culture and community, industrial and supply chain network, access to raw materials and skilled workforce, support from the government, and the cost of living in Montreal.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.015 | 0.013 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".