Fashion interest as a driver for consumer textile waste management: reuse, recycle or disposal
Bibliographic record
Abstract
Abstract Past studies have considered the impact of fashion on consumer textile disposal behaviour, but have focused mainly on drivers of clothing waste. There is a lack of research that examines consumer attitudes towards fashion and their disposal methods. This study conducted an online survey of 410 people in Ontario, Canada with varying demographic characteristics to assess how they currently manage their textile waste including resell, swap, take‐back, donation and disposal. Respondents were asked about their fashion interest and shopping frequency and were assigned a fashion index value. The fashion index value is not a means of grouping consumers but is instead a continuum to model interest in fashion, with one extreme representing fashion consumers and the other representing non‐fashion consumers. Statistical analysis was then used to establish whether there is a link between textile waste behaviour and fashion index. The results indicate that consumers with a high fashion index (i.e. fashion consumers) and consumers with low fashion index (i.e. non‐fashion consumers) manage their textile waste differently. While the majority of participants donate and dispose of unwanted clothes, fashion consumers are more interested and more likely to participate in alternative methods (e.g. resell, swap, and take back) for removing unwanted textiles. Although fashion consumers produce more textile waste than non‐fashion consumers, textile consumption cannot be directly equated with textile waste since fashion consumers were found to have a lower disposal rate than non‐fashion consumers (38 percent to 50 percent, respectively). The distinct disposal characteristics of fashion and non‐fashion consumers (i.e. interest and willingness to participate in alternative channels) allows strategies to be tailored accordingly so that the amount of waste going to landfill can be reduced.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".