Bibliographic record
Abstract
The fashion industry undergoes constant evolution driven by changes in consumer preferences. Fast fashion is loved by many by selling large quantities of different styles of clothing cheaply and updating them very quickly. However, with the increasing popularity of fast fashion as consumption trends, environmental concerns and human rights are gaining more attention. Issues such as resource overexploitation and injustice for workers are becoming prominent topics of discussion among the public. In this context, this study aims to provide a comprehensive analysis of the ecological and human impact of minimalist clothing and fast fashion, with a focal point highlighting the significance of environmental issues in the fashion industry. The study will examine key aspects such as resource consumption, waste production, and labour rights to discuss the impact of these fashion choices on the environment and humanity. The study concludes that the negative impacts of fast fashion are not only environmental but also human rights. These findings emphasize the need for the fashion industry to shift towards sustainability. By highlighting the environmental impact of fast fashion, this study seeks to inspire positive changes in consumer behavior and contribute to protecting workers’ rights and ecosystems. Stakeholders such as consumers, brands and policymakers must unite to turn these insights into practical action to create a fairer, greener 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 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.014 | 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".