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Record W4252464108 · doi:10.32920/ryerson.14665440

Challenging the fast fashion product: a method for use-value apparel design

2021· preprint· en· W4252464108 on OpenAlexaff
Jennifer Triemstra-Johnston

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldArts and Humanities
TopicFashion and Cultural Textiles
Canadian institutionsToronto Metropolitan UniversityYork University
Fundersnot available
KeywordsClothingFast fashionStatus quoSustainable designProduct designSustainable ValueSustainabilityProduct (mathematics)Value (mathematics)Fashion designFunction (biology)Design for the EnvironmentProcess (computing)TypologyArchitectural engineeringComputer scienceBusinessEngineeringProcess managementSociologyEconomicsPolitical science

Abstract

fetched live from OpenAlex

Sustainable apparel design is a discipline based on challenging the status quo. Applying an interdisciplinary approach, this paper integrates the methodologies of research through practice, sustainable design, and material culture to challenge contemporary products found on the fast fashion market. Exploring use-value as an avenue for sustainable design, a typology is developed addressing the identifiers of function, aesthetics, expression, and durability as a method for generating sustainable solutions. The interpretative methods of material culture are adapted into a use-value challenge as a process for establishing sustainable and unsustainable elements embedded within a product. The concept of best practices is introduced as a procedure for assessing the solutions for the creation of alternative prototypes. A case study, challenging children’s princess costumes found on the contemporary market, provides an example of how the use-value method for apparel design can be applied to fast fashion products.

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 imitation

Not 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.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.014
Threshold uncertainty score0.047

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.012
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.002
Science and technology studies0.0020.006
Scholarly communication0.0060.005
Open science0.0020.005
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0140.003

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 head0.158
GPT teacher head0.305
Teacher spread0.147 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreMethods

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

Citations4
Published2021
Admission routes1
Has abstractyes

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