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Record W3088066380 · doi:10.15353/jirr.v3.1624

The Impact of Fast Fashion on Women

2020· article· en· W3088066380 on OpenAlexaffvenue
Andrea Chang

Bibliographic record

VenueJournal of integrative research & reflection · 2020
Typearticle
Languageen
FieldArts and Humanities
TopicFashion and Cultural Textiles
Canadian institutionsMcMaster University
Fundersnot available
KeywordsFeminization (sociology)Fashion industryFast fashionClothing industryPrivilege (computing)ClothingBusinessFashion designPosition (finance)MarketingSociologyPolitical scienceGender studiesLaw

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.230
Threshold uncertainty score0.910

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.143
GPT teacher head0.418
Teacher spread0.275 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations6
Published2020
Admission routes2
Has abstractyes

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