Fashioning sustainability: drawing lessons from the fair trade coffee industry
Bibliographic record
Abstract
Due to the fashion industry’s global reach, spanning many jurisdictions, regulations are difficult to implement, monitor and enforce. Strict voluntary initiatives that focus on raising consumer awareness, thereby creating greater demand for eco fashion have greater potential to lead to reform within the fashion industry. To do so, voluntary initiatives must include clear labeling of ‘eco’ products and designer input, and include strict guidelines for company and designer standards. Standards must take the entire life cycle of a garment into consideration. Fashion can apply lesson from the fair trade coffee industry by appealing to consumers based on ethics and environmental responsibility through a trusted consumerfacing label. Fair trade was successful, in part, due to their recognizable label. Fair trade type certifications are most often business to consumer facing and provide consumers with the environmental and social information on the benefits of purchasing fair trade. Fair trade certification models have capitalized on large retailer involvement, allowing certifications to become mainstreamed and therefore more accessible for 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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.018 |
| Scholarly communication | 0.011 | 0.014 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.005 |
| 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".