Bibliographic record
Abstract
The growing prevalence of textile waste, largely driven by the fast-fashion model, necessitates a shift towards a circular economy that prioritises the reuse of materials. In Canada, the textile donation landscape is primarily managed by charities and for-profit organisations, which often rely on public donations to generate revenue. Despite high donation rates, many items received by thrift stores are unsuitable for resale, creating challenges for organisations dependent on volunteer labour. This paper discusses the Treat & Clean pilot study, part of a larger investigation into the quality and type of clothing donations received in Canadian thrift stores. The project aimed to assess the feasibility of rejuvenating so-called "substandard" clothing through cleaning and repair techniques, allowing these items to be resold. In total 4946 textile items donated to two large non-profit thrift organisations at nine separate store locations within Alberta and Saskatchewan were sorted. A significant portion of the donations were deemed unfit for immediate resale. A subset of sorted items (N=2271) was analysed off-site and considered for the Treat & Clean pilot. The effectiveness of the pilot was evaluated by analysing the success of treatments (e.g., stain removal) and tracking the resale of treated items. Results indicate that enhancing the quality of donations through simple cleaning and repair methods can increase the likelihood of their sale, thereby promoting sustainability in local communities. This study highlights the importance of increasing consumer awareness regarding donation quality.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.120 | 0.037 |
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".