Driving in the wrong lane: towards a longer life-span of cars
Bibliographic record
Abstract
Within the context of product longevity, one especially impactful and ubiquitous product demands further research: the car. Car longevity has been addressed in the context of product life extension and product lifetime optimisation but there have been a few studies on car longevity in the context of business and none specifically from an industrial design context. This paper presents initial findings from preliminary interviews with key industry representatives such as car designers and engineers. It discusses the barriers to and opportunities for designing a car with a longer life-span. This and further data will later be analysed in order to produce a design framework to inform car \ndesigners on life-span and usage optimization through design. Strategies such as increased longevity or use-intensity can potentially reduce the throughput - and thereafter the consumption - of cars. Such a shift in the automotive sector would support the transition from a linear economy to a more sustainable one. The initial findings, however, suggest that a longer life car is not an uncompromised solution and important concessions would have to be made in order to make this an acceptable \nproduct.
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 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".