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Record W4389777888 · doi:10.1007/s43615-023-00322-w

From Simplistic to Systemic Sustainability in the Textile and Fashion Industry

2023· article· en· W4389777888 on OpenAlexaff
Olli Sahimaa, Elizabeth M. Miller, Minna Halme, Kirsi Niinimäki, Hannu Tanner, Mikko Mäkelä, Marja Rissanen, Anna Härri, Michael Hummel

Bibliographic record

VenueCircular Economy and Sustainability · 2023
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicSustainable Supply Chain Management
Canadian institutionsWestern University
FundersStrategic Research CouncilAalto-YliopistoAcademy of Finland
KeywordsSustainabilityTextile industryProduct (mathematics)BusinessConsumption (sociology)HierarchyValue (mathematics)Natural resourceResource (disambiguation)Resource consumptionEnvironmental economicsTextileIndustrial organizationNatural resource economicsEconomicsEcologyComputer scienceSociologyMarket economy

Abstract

fetched live from OpenAlex

Abstract The fast fashion industry is notorious for wicked environmental and social problems, such as exploitative resource use, high amounts of waste, excessive pollution, below-living wages and unsafe working conditions. Addressing these problems calls for a systemic view on the industry with the goal of minimising the intake of natural resources into the system as well as the output of waste. However, thus far, most solution attempts have turned out simplistic and insufficient to nudge the industry to more sustainable practices at scale. We examine the textile and fashion system at the three different levels—the product, industry and socio-ecological system levels—and show the inadequacy of the current sustainability-driven practices in the field. As an alternative, we propose systemic solutions, geared toward long material and product lifetimes, that have the potential to trigger adaptive responses throughout different actors in the system and across all three levels. These systemic solutions operationalise a circular value retention hierarchy coupled with a sufficiency-based consumption philosophy.

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)0.003
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.474
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.011
GPT teacher head0.233
Teacher spread0.223 · 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.

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

Citations19
Published2023
Admission routes1
Has abstractyes

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