Transparency Is the New Black: A Systemic View of Fashion Sustainability in Canada and Abroad
Bibliographic record
Abstract
As consumers buy more and wear less, manufacturers have no choice but to reduce production costs to remain competitive, even if it means exacerbating local pollution and human rights issues.Production requiremnets Production costs Production quality Investment in effeciencies Consumer demand for apparel Working conditions Human rights violations Social and environmental sustainability Products sent to landfills Product lifespan Environmental pollution Production waste Production quality and consumer demand R Power dynamics make it difficult even for governments to curb the environmental and labour impacts in their communities.Public health impacts Environmentally and ethically harmful manufacturing practices Environmental and labour regulation in exporting country Social and environmental wellbeing Supplier/country atrractiveness for manufacturing Economic impacts in exporting country Fast fashion companies relocating their manufacturing Manfacturing costs Industry regulation B Threat of Exit B How did the fashion sustainability conversation come about?Use of low quality materials Opaque supply chain Unsafe and unethical working conditions Water and air pollution Accumulation of textile waste
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.016 | 0.017 |
| Scholarly communication | 0.016 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 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".