Bibliographic record
Abstract
The constructed gender roles and stereotypes of women position them to be uniquely impacted by the fast fashion industry because of the feminization of the fashion industry as a whole. They are disproportionately employed in the sweatshops of the garment industry, and also are mainly targeted as the consumers of fast fashion. However, because of the different levels of privilege that consumers and garment workers hold, although they are both affected by the fast fashion industry more so than their male counterparts, gender plays two different roles in these two different situations. Ultimately, many modern fast fashion critiques take a neoliberal stance in putting the responsibility on these young fast fashion consuming women to stop the fast fashion industry. However, alternate literature suggests that other actors have immense responsibility that is often overlooked. Thus, although these relatively privileged young women do have some responsibility in the horrors of the fast fashion industry, the feminization of responsibility for the practices of the industry are unfair. When a highly feminized industry like the fast fashion one becomes problematic, the responsibility for positive change is also placed upon females. The switch to ethical and sustainable fashion as the primary, and only, type of clothing to purchase is imperative. However, this switch should not only be the consumers’ burden, but rather that of the fashion industry as a whole.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.011 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".