From Simplistic to Systemic Sustainability in the Textile and Fashion Industry
Bibliographic record
Abstract
Abstract The fast fashion industry is notorious for wicked environmental and social problems, such as exploitative resource use, high amounts of waste, excessive pollution, below-living wages and unsafe working conditions. Addressing these problems calls for a systemic view on the industry with the goal of minimising the intake of natural resources into the system as well as the output of waste. However, thus far, most solution attempts have turned out simplistic and insufficient to nudge the industry to more sustainable practices at scale. We examine the textile and fashion system at the three different levels—the product, industry and socio-ecological system levels—and show the inadequacy of the current sustainability-driven practices in the field. As an alternative, we propose systemic solutions, geared toward long material and product lifetimes, that have the potential to trigger adaptive responses throughout different actors in the system and across all three levels. These systemic solutions operationalise a circular value retention hierarchy coupled with a sufficiency-based consumption philosophy.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.018 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".