Community Transport’s Dual Role as a Transport and a Social Scheme: Implications for Policy
Bibliographic record
Abstract
Community transport comprises diverse local, not-for-profit, and primarily volunteer-run transport schemes that operate across the United Kingdom. These schemes support the travel needs of thousands of people, most of whom are older, live in rural areas, and have few other transport options. Further, this transport sector is unique in that most schemes are designed, created, and run by older people themselves. And yet, community transport has thus far received relatively little attention in both policy and research. Using semi-structured interviews with community transport providers in Oxfordshire, this paper proposes community transport as a practice guided by phronesis and argues that it has been made to hold a dual role as both a transport and a social scheme. The transport it provides is unique in being made low-cost, flexible, and functionally accessible. It has also been made into a social scheme as it helps those with few other options, provides benefits that extend beyond the transport realm, and fosters community. Though this dual role means that community transport has many cross-sectoral benefits, this type of service provision is found to be overlooked in both national and local policy, which has enabled the constitutive role of phronesis in community transport. Given this, there are challenges ahead for the sector in both ensuring its sustainability and maintaining its ability to respond closely to users' needs.
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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 | 0.025 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.011 | 0.057 |
| Scholarly communication | 0.020 | 0.041 |
| Open science | 0.003 | 0.018 |
| Research integrity | 0.016 | 0.012 |
| Insufficient payload (model declined to judge) | 0.010 | 0.000 |
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.
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".