How Fast Should Fashion Really Be? An Investigation into Whether It Is Possible for ‘Fast Fashion’ to Adapt to Meet Social Sustainability Goals
Bibliographic record
Abstract
In the last decade, there have been a number of fatal disasters in garment factories across the world, with many of these factories producing garments for the ‘fast fashion’ industry. With the ‘fast fashion’ industry continuing to grow at an exponential rate, it is important, now more than ever, to understand how garment workers at the heart of this growth can be protected in a socially sustainable way. The motivation of this study is to look into ways of empowering producers of the garments, not just those who wear them. The objective of this research is to understand whether it is possible for ‘fast fashion’ to meet social sustainability goals, or whether the model is inherently unsustainable. To address this question, a systematic literature review was conducted, which maps literature on ‘fast fashion’ and social sustainability. This review looks at the two concepts independently, then draws the literature together to explain the relationship, and outlines approaches that can be taken to ameliorate it. The paper presents an argument for how policymakers, businesses and consumers can play a part in making fast fashion more socially sustainable in the hope of one day being able to empower all. The conclusion of this review is that fast fashion is only capable of exhibiting ‘weak’ sustainability, and that systemic change would be needed across stakeholders to address social sustainability goals effectively.
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 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".