Mitigating Trendy Cheap Fast Fashion's Negative Impact
Bibliographic record
Abstract
Three studies are carried out in an attempt to provide a picture of clothing consumption and knowledge of fast fashion among young consumers, and investigate possibilities for more sustainable choices through analyses of the second-hand clothing market. The first study collects data from different second-hand clothing markets, whether direct from owner or through a second seller. Savings are calculated by scraping original and sale prices on regular markets. Content analyses of second-hand markets show a wide variation in discounts depending upon the type of clothing and channel used to purchase. We find independent resellers offer significant savings on higher quality clothing, but reselling used fast-fashion is not an attractive option due to its initial low price point. The second and third studies assess the attitude, behavior, and knowledge of fast fashion among young consumers and the possibility of education to decrease fast fashion consumption. These studies document the desire for fashionable clothing and expose the limited budget among young consumers. Some respondents spend all their discretionary income on clothing, and many times, purchased items are never worn. There is some indication that educating young consumers about real environmental impacts might shift purchases from quantity to quality, but educating consumers about the harm of fast fashion may be a slow difficult task.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 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".