An investigation into the clothing repair behaviour of fashion-sensitive consumers
Bibliographic record
Abstract
Pro-environmental practices are an essential component of consumer culture in this century. Consumers are endeavouring to be eco-friendlier than ever, yet extant research suggests that consumers who are sensitive to rapidly changing fashion trends are mostly unwilling to repair out-of-fashion clothes, and consequently, unsustainably dispose of their unwanted garments. The scarcity of empirical research on the mediating effects of fashion-sensitive consumers’ pro-environmental practices on their clothing repair behaviour is thus the fundamental motivation for this research. The primary research data in this study were collected using a cross-sectional online survey of 1013 adult consumers from four Anglosphere countries, including Australia, Canada, New Zealand, and the United States of America. A total of 515 female, and 498 male, fashion-sensitive consumers from these countries indicated that general pro-environmental practices have a positive effect on their clothing repair behaviour. Specifically, older male consumers with higher education levels are more likely to engage in clothing repair behaviour than are female consumers. Canadian consumers are the most likely, by country, to engage in clothing repair behaviour, based on the mediating effect of their pro-environmental practices. Conceptually, this research sheds light on the association between pro-environmental practices of fashion-sensitive consumers and their clothing repair behaviour. Pragmatically, the findings of this research provide insights for social marketers in developing effective clothing repair strategies for fashion-sensitive consumers.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".