OPPORTUNITIES AND CHALLENGES OF SUSTAINABLE LOCAL WOOL PRODUCTION IN QUEBEC: AN EXPLORATORY STUDY OF SUPPLY CHAIN AND DEVELOPMENT STRATEGIES FOR THE FASHION INDUSTRY
Bibliographic record
Abstract
The fashion industry has a negative impact on the environment and society, leading consumers to seek more responsible alternatives. As a natural and durable material, wool is gaining popularity, but local wool sourcing is often overlooked. This article explores the opportunities for sustainable local wool production in Quebec and the challenges in developing a new local wool supply chain. The study draws on a pilot project by Fibershed Quebec, which collected feedback from 75 participants to better understand the challenges faced by new actors in the fashion industry in this logistics chain. The results show that local companies in the textile and apparel ecosystem need to assess best practices and adopt new perspectives and strategies to support local wool production and strengthen the local supply chain. An additional survey was conducted to capture the real challenges of creating a fashion collection using local fibers for a specific market. The results emphasize the importance of training and support for new actors in the wool and fashion industry to help them overcome obstacles and succeed in a highly competitive market. Finally, the article explores the challenges and opportunities of sustainable local wool production in Quebec. It examines current efforts to diversify wool production and analyzes the challenges and opportunities facing local businesses. This study highlights the importance of wool production in Canada. It highlights the need for scientists and stakeholders in the sheep industry to find new ways to make this activity profitable.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".