Bibliographic record
Abstract
This study attempted to investigate present condition and product planning of global eco-fashion brands that harmonize fashion and sustainability. As research subjects, this study selected 97 oversea fashion brands mentioned in books related to eco-fashion, Black(2011), Brown(2010), Fuad-luke(2009). As for research methods, materials and ethical practices of these selected 97 brands through literature data and their internet site homepages. This study analyzed oversea eco-brands collected 26 British brands, 22 American brands, 36 European brands such as Germany, France, Italy, Sweden, Spain, Finland and so on, except Britain and 13 other regions including Japan, India, Canada, Mexico, and New zealand. In conclusion, the product planning characteristics of these oversea eco-fashion brands can be summarized as follows; community and fair trade, ecological and slow design, recycle, reuse, redesign, and new eco-models. Firstly, brands of ‘community and fair trade’ manufactured products through fair trade and local community’s artisan by ethical practices with organic fabrics. Secondly, brands of ‘ecological and slow design’ pursued timeless design and multi-functional design as luxury eco-fashion styles. They used organic textiles, hemp, bamboo, soya, tencell, sea cell, and self-sustaining plants. Thirdly, brands of ‘recycle, reuse, redesign’ aimed for upcycling high-end fashion and used vintage clothes, textile scraps, PET, parachutes, tires, safety belts, advertising banner and so on. In addition, brands of ‘new models as eco-fashion’ suggested zero-waste cutting, recycling over-printing technology, new sustainable business model, and ethical practices in the supply chain of the fashion industry.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.011 | 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".