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Record W4309617719 · doi:10.1177/10126902221138033

Reduce, re-use, re-ride: Bike waste and moving towards a circular economy for sporting goods

2022· article· en· W4309617719 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueInternational Review for the Sociology of Sport · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicMunicipal Solid Waste Management
Canadian institutionsUniversity of British ColumbiaQueen's University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsCircular economyObsolescenceSustainable consumptionConsumption (sociology)BusinessProduction (economics)Sustainable developmentExtended producer responsibilitySustainabilityMarketingCommerceEconomicsEnvironmental economicsPolitical science

Abstract

fetched live from OpenAlex

What happens to our sporting goods when we are done with them? Even though Sustainable Development Goal 12 focuses on responsible consumption and production, very few in the sports industry (and academy) have asked this question. With environmental degradation now a daily concern around the world, we can no longer produce and consume sporting goods without considering the end-of-use stage for these products. This study focuses on the bike and its role in global waste accumulation through various forms of planned obsolescence. Through interviews with experts in and around the bike industry and waste management, we provide insight into the environmental barriers that are structural and specific to the bike industry. We then advocate for extended producer responsibility and the circular economy as an imperfect but radical alternative future.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.815
Threshold uncertainty score0.938

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.047
GPT teacher head0.321
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