As the Clothing Industry is a Major Polluter, Sustainable Fashion is Rising
Bibliographic record
Abstract
That the fashion industry is one of the biggest polluters in the world is one of the industry’s best-kept secrets — it uses a quarter of the chemicals produced globally, and its share of the world’s CO2 emissions is expected to rise from 2 percent to 26 percent in the next 30 years. Developing nations — where many factories are located — are most affected. Every year in Bangladesh, for example, tanneries dump enough toxic waste into rivers to fill three Olympic-sized swimming pools. Advocates and some in the fashion world are working to not only get the word out, but to revolutionize the way the industry does business. Younger consumers are more environmentally conscious, and newer brands are emerging that emphasize sustainability. Many established clothing companies — like Gap and Burberry — are adding environmentally conscious lines and dedication to sustainability, as awareness spreads in the industry and to respond to consumer consciousness. Link to capstone project: https://medium.com/@gabe.herman/as-the-clothing-industry-is-a-major-polluter-sustainable-fashion-is-rising-5a9e48b94d4d
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.313 | 0.146 |
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".