Bibliographic record
Abstract
2014 marks the 25th birthday of the World Wide Web. We have seen some remarkable developments as part of the digital age revolution in the last quarter of a century. These have taken place concurrently with a motor age that is possibly past its prime. A number of major motor manufacturers have faced disappointing sales or financial crisis alongside several countries seeing a halt to the historic trend of growing car use. The co-existence of the motor age and the digital age prompts this paper to consider the hypothesis that society is undergoing a fundamental transition from a regime of automobility to something significantly different. The paper considers what has characterized the motor age and proceeds to examine the digital revolution and how this is changing people’s means to access people, goods, services and opportunities. The range of interactions between the motor age and the digital age are addressed, underlining the difficulty in establishing the net consequence \nof one for the other. The new debates concerning ‘peak car’ are considered in which the digital age is identified as potentially one key factor responsible for observed changes in car use. The paper then focuses upon a socio-technical conceptualization of society known as the Multi-Layer Perspective to examine its hypothesis. Support or not for the hypothesis is not, as yet, established. Transport’s future in the digital age is uncertain and the paper sets out some views on resulting policy considerations and research needs.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.000 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.019 | 0.003 |
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".