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Record W2610283054 · doi:10.1016/j.eist.2017.04.002

Systems of practice and the Circular Economy: Transforming mobile phone product service systems

2017· article· en· W2610283054 on OpenAlexaff
K. Hobson, Nicholas Lynch, David Lilley

Bibliographic record

VenueEnvironmental Innovation and Societal Transitions · 2017
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicService and Product Innovation
Canadian institutionsMemorial University of Newfoundland
FundersEngineering and Physical Sciences Research Council
KeywordsMobile phoneProduct (mathematics)BusinessMarketingKey (lock)Service (business)CentralityPhoneProduct-service systemBusiness modelKnowledge managementComputer scienceTelecommunicationsComputer security

Abstract

fetched live from OpenAlex

Of late, policy and research attention has increasingly focused on making the Circular Economy a reality. A key part of this agenda is the creation of Sustainable Product Service Systems (SPSS) that meet consumers’ needs whilst lessening negative environmental impacts. Although the SPSS literature has grown recently, key aspects require further examination. In response, this paper discusses empirical research exploring consumers’ reactions to a novel, hypothetical mobile phone SPSS, utilizing qualitative methods that included ‘business origami’. It examines consumers’ knowledge about current mobile phone life cycles, and responses to the proposed SPSS, drawing on a ‘systems of practice’ framework to discuss the potential for significant changes in phone purchase and use. It outlines barriers to alterations in practices, underscoring the centrality that connectivity and data storage now have in many peoples’ daily lives, which have for some become clustered around the capabilities and accessibility of the mobile phone.

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)0.019
metaresearch head score (Gemma)0.025
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.019
Threshold uncertainty score0.102

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0190.025
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0030.003
Science and technology studies0.0070.069
Scholarly communication0.0160.019
Open science0.0020.012
Research integrity0.0040.004
Insufficient payload (model declined to judge)0.0050.001

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 head0.018
GPT teacher head0.235
Teacher spread0.216 · 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

Citations89
Published2017
Admission routes1
Has abstractyes

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