Developing scenarios for product longevity and sufficiency
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.005 | 0.012 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".