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Record W2766605684 · doi:10.3233/978-1-61499-820-4-108

Developing scenarios for product longevity and sufficiency

2017· book-chapter· en· W2766605684 on OpenAlexfundno aff
Emma Dewberry, Leila Sheldrick, M.A. Sinclair, Mariale Moreno, Charalampos Makatsoris

Bibliographic record

VenueResearch in design series · 2017
Typebook-chapter
Languageen
FieldBusiness, Management and Accounting
TopicSustainable Supply Chain Management
Canadian institutionsnot available
FundersCranfield UniversityTrent UniversityNottingham Trent University
KeywordsLongevityProduct (mathematics)Environmental scienceBiologyMathematicsGenetics

Abstract

fetched live from OpenAlex

This paper explores the narrative of peoples' relationships with products as a window on understanding the types of innovation that may inform a culture of sufficiency. The work forms part of the ‘Business as Unusual: Designing Products with Consumers in the Loop’ [BaU] project, funded as part of the UK EPSRC-ESRC RECODE network (RECODE, 2016) that aims to explore the potential of re-distributed manufacturing (RdM) in a context of sustainability. This element of the project employed interviews, mapping and workshops as methods to investigate the relationship between people and products across the product lifecycle. A focus on product longevity and specifically the people-product interactions is captured in conversations around product maintenance and repair. In exploring ideas of ‘broken’ we found different characteristics of, and motivations for, repair. Mapping these and other product-people interactions across the product lifecycle indicated where current activity is, who owns such activity (i.e. organisation or individual) where gaps in interactions occur. These issues were explored further in a workshop which grouped participants to look at products from the perspective of one of four scenarios; each scenario represented either short or long product lifespans and different types of people engagement in the design process. The findings help give shape to new scenarios for designing sufficiency-based social models of material flows.

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)0.006
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.713
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
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 head0.213
GPT teacher head0.356
Teacher spread0.143 · 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 designTheoretical or conceptual
Domainnot available
GenreOther

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

Citations15
Published2017
Admission routes1
Has abstractyes

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