For waste’s sake: Stakeholder mapping of circular economy approaches to address the growing issue of clothing textile waste
Bibliographic record
Abstract
By now, it is well established that the fashion industry faces several social and environmental sustainability issues, including the growing problem of clothing textile waste. In recent years, the concept of circular economy (CE) has been put forth as a solution to drive the industry towards a more sustainable future, including as a strategy to reduce clothing textile waste. However, currently there is a gap in our understanding of how circular approaches are enacted by different stakeholders and if/how stakeholders are working together, especially when it comes to post-consumer clothing textile waste. To remedy this gap, this conceptual article draws on a wide range of secondary resources to propose a conceptual framework based on stakeholder mapping. The framework aims to help understand who is responsible for post-consumer textile waste and how they interact and work together, driven by three key questions: where do responsibility(ies) lie in addressing the growing challenge of textile waste, what actions are currently being taken across supply chains and stakeholders to address textile waste and what are the opportunities and challenges in conceptualizing CE practices through a stakeholder mapping approach? In exploring actions across four key stakeholder groups (policy-makers, fashion industry, clothing textile recyclers and actors from the not-for-profit sector), the need for engagement and collaboration across stakeholders, investment in recycling technology and infrastructure, and policy leadership are identified as key challenges facing the industry as it seeks to redress social and environmental challenges.
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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 | 0.019 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".