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Record W3111780172 · doi:10.1080/15487733.2020.1829848

Will COVID-19 support the transition to a more sustainable fashion industry?

2020· article· en· W3111780172 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueSustainability Science Practice and Policy · 2020
Typearticle
Languageen
FieldArts and Humanities
TopicFashion and Cultural Textiles
Canadian institutionsKwantlen Polytechnic UniversityThompson Rivers University
FundersVetenskapsrådet
KeywordsSustainabilityCoronavirus disease 2019 (COVID-19)Transition (genetics)Consumption (sociology)BusinessSocial sustainabilitySocioeconomic statusSupply chainSustainable developmentFast fashionEconomicsMarketingSociologyPolitical scienceClothingSocial science

Abstract

fetched live from OpenAlex

In this policy brief, we examine the impact of COVID-19 on sustainability initiatives in the fashion industry. We ask whether COVID-19 is likely to support the transition to a more sustainable fashion industry. In answering this question, we utilize a framework for examining sustainability along the fashion-supply chain, highlighting the opportunities and challenges for a sustainable transition with respect to design, production, retail, consumption, and endof-life. At each step, we also consider socioeconomic dimensions with regard to social impacts, employment, and gender. In doing so, we argue that any meaningful shift toward sustainability and a just transition must recognize social and environmental challenges as interconnected, addressing structural inequalities.

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.

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)0.013
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.619
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.002
Scholarly communication0.0010.004
Open science0.0000.000
Research integrity0.0000.000
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 head0.036
GPT teacher head0.346
Teacher spread0.310 · 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