Care‐driven informality: The case of community transport
Bibliographic record
Abstract
Abstract Nation‐wide cuts to bus subsidies have led to reduced service in rural communities in the UK, leaving those who do not have access to a car – most of whom are older, have a disability, or have a low income – with few other options to meet their travel needs. This has resulted in greater demand on community transport, small‐scale, local, and community‐based transport schemes that are run by the not‐for‐profit sector and are primarily volunteer‐run. Drawing on 28 interviews conducted with volunteers and staff from community transport schemes across Oxfordshire, this paper describes the provision of community transport schemes at the intersection of informal transport and an ethics of care. This sector is posited as informal, however; unlike many informal transport schemes, community transport is non‐entrepreneurial. Instead, these schemes emerge from the community and are care‐driven. Volunteers who run these schemes all provide skilled labour that is a practice of caring about, caring for, or care giving. This framing highlights the undervaluing of community transport. Indeed, the labour and schemes are underfunded and lack recognition. This study therefore emphasises the socio‐political nature of community transport and shows the importance of supporting caring transport services. It concludes by discussing how this undervalued sector might be re‐valorised so that it can continue to support those with few other transport options.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.027 | 0.026 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".