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Record W4211036918 · doi:10.32920/ryerson.14657976.v1

Putting the brakes on fast fashion: understanding the gap between sustainable awareness and action

2021· preprint· en· W4211036918 on OpenAlexaff
Shelley Haines

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldBusiness, Management and Accounting
TopicEnvironmental Sustainability in Business
Canadian institutionsToronto Metropolitan UniversityUniversity of Toronto
Fundersnot available
KeywordsSustainable consumptionMargin (machine learning)CertaintyAction (physics)Consumption (sociology)Sample (material)Sustainable agricultureSustainable developmentBusinessSustainabilityPsychologyEnvironmental economicsMarketingEconomicsMicroeconomicsSociologyComputer sciencePolitical scienceSocial scienceMathematicsProduction (economics)

Abstract

fetched live from OpenAlex

Using a mixed-methods approach, this study addressed two related research questions. First, is there a discrepancy between consumers’ sustainable values and sustainable behaviour? While separate studies suggest that this disconnect exists, it has not been empirically validated within the same individuals in a single study. Second, if this discrepancy exists, what are the barriers to sustainable fashion consumption? It was found that, on average, subjective sustainable values were higher than objective sustainable behaviour. A one-sample t-test revealed that this difference was significantly different from zero, with a 99.9% margin of certainty. To identify the barriers that might explain why sustainable values do not appear to be translating into sustainable behaviour, interviews were conducted in participants’ wardrobes based on guided tour and personal inventory frameworks. Style, social repercussions, and wardrobe maintenance and disposal behaviour were the most frequently reported barrier-related themes. Results are discussed in light of promoting sustainable fashion consumption.

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)0.035
metaresearch head score (Gemma)0.039
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.035
Threshold uncertainty score0.184

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0350.039
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.003
Science and technology studies0.0040.009
Scholarly communication0.0120.011
Open science0.0020.007
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.073
GPT teacher head0.274
Teacher spread0.201 · 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 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

Citations0
Published2021
Admission routes1
Has abstractyes

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