Unpicking the Gender Gap: Examining Socio-Demographic Factors and Repair Resources in Clothing Repair Practice
Bibliographic record
Abstract
Increased fashion consumption spurred by fast fashion has led to excessive textile waste, giving rise to a global crisis as textile waste pollutes land and waterways, while landfill and incineration contribute to global greenhouse gas emissions. Extending a product’s life for as long as possible is a core principle of the circular economy (CE) to ensure that the maximum value of the original product is realized over its lifetime. As such, repair is an essential component of a CE because it supports the preferred waste hierarchy elements of reduce and reuse, with recycling being the last resort in a CE necessary to close resource loops. Consumers are an essential enabler of a CE; therefore, it is critical to understand consumers’ characteristics in the context of behaviors such as repair. The purpose of this study was to examine the role of gender on engagement in clothing repair practices; women have often only been the focus of clothing repair studies. An online survey was conducted to collect responses from Canadian and U.S. consumers (n = 512). Findings showed that self-repair was the most common form of clothing repair, with women being more highly engaged in self-repair practices, increasing with age. Paid repair is the type of repair that has the lowest level of engagement, and there are only negligible differences between the genders. Men utilize unpaid forms of repair more than women. However, among the youngest age group (18–24), both genders are equally likely to have clothing repaired for free. Gender gaps exist, but opportunities for increased utilization in repair can be created to encourage full participation within a CE. In particular, the findings point to the importance of increasing repair activities amongst men and younger 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 | 0.003 | 0.010 |
| 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 | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".