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Record W3022639051 · doi:10.1111/ijcs.12590

Impact of physical condition on disposal and end‐of‐life extension of clothing

2020· article· en· W3022639051 on OpenAlexaffabout
Lauren M. Degenstein, Rachel H. McQueen, Lisa S. McNeill, Robert P. Hamlin, Sarah Wakes, Linda Dunn

Bibliographic record

VenueInternational Journal of Consumer Studies · 2020
Typearticle
Languageen
FieldArts and Humanities
TopicFashion and Cultural Textiles
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsClothingDispose patternBusinessQuality (philosophy)Investment (military)Extension (predicate logic)Operations managementWaste managementEngineeringComputer science

Abstract

fetched live from OpenAlex

Abstract Clothing waste is an increasing global problem as “disposable” fashion items are consumed and discarded at rapid rates. Low‐quality fashion garments are easily damaged and thrown out due to the low initial investment and replacement cost of other items. Previous research has found physical damage to be a common reason for clothing disposal; however, the degree to which damage plays a role in disposal decisions has not been studied. Therefore, using a survey‐based, pre‐experimental design, this research examined the extent to which varying levels of garment physical damage influences consumer disposal decisions and garment life extension practices in Edmonton, Canada. Results indicated that damage severity plays a significant role in how respondents choose to dispose, or otherwise deal with, their unwanted clothing. Garment quality and type were also shown to predict disposal method and end‐of‐life extension strategies.

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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.103
Threshold uncertainty score0.206

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.076
GPT teacher head0.350
Teacher spread0.274 · 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 designObservational
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

Citations57
Published2020
Admission routes2
Has abstractyes

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