The role of resources in repair practice: Engagement with self, paid and unpaid clothing repair by young consumers
Bibliographic record
Abstract
As repair can lead to a reduction in clothing consumption and textile waste, repair is essential toward improving the lifetime sustainability of garments and achieving a circular economy. In the literature, common barriers preventing one from conducting garment repairs have been identified. This research re-conceptualizes common repair barriers as repair resources that comprise the skills, tools, priority, and perceived expense that may motivate one toward self-repair, paid and unpaid repair of clothing. A survey of 523 young Canadian consumers (aged 18–34 years) was conducted, in order to examine the impact selected demographic factors and repair resources have on their propensity to carry out different forms of clothing repair. Independent variables were demographic factors and four repair resources, dependent variables were three repair practices. Hierarchical linear regression analyses showed that women were more likely to engage in self-repair, while no gender differences appeared in paid and unpaid repair. Increasing age leads to increased self and paid repair; whereas unpaid repair was more likely to be utilized by the younger consumers. Three repair resources of skills, tools, and priority toward repair strongly predict self-repair. Paid repair is more likely to be utilized if the cost for professional repair services is not perceived to be prohibitive. Young consumers who utilize unpaid repair, while not having the skills, do have access to repair tools and access to skilled resource-rich individuals. The results from this study have implications toward fashion brands, policy and communities in promoting and encouraging various forms of repair practice.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".