Learning from the Best and Worst: Problems, Prospects and Policy Implications from Global Benchmarking of Urban Passenger Transport Sustainability in Greater Manchester and the Leicester Metropolitan Area, UK
Bibliographic record
Abstract
Studies comparing and benchmarking cities on transport and planning have been undertaken for decades. The unique methodology in this paper is explained and then applied to the Greater Manchester (GM) and Leicester (LM) metropolitan areas in the UK. The data cover land use, wealth, transport infrastructure, mobility patterns, energy use and selected externalities. The paper asks: How do the Greater Manchester and Leicester Metropolitan Areas compare with each other and to a sample of global cities in the sustainability of their urban passenger transport systems, what are the key factors that underpin their automobile dependence and what might be done to improve the prospects for public transport, walking and cycling? The answer is presented as standardised indicators comparing GM and LM to metropolitan areas in the USA, Canada, Australia, Europe and Asia (averages), as well as ten Swedish cities plus Freiburg-im-Breisgau, Germany. Both UK metropolitan areas rank poorly on most transport factors, especially public transport and cycling rates. They have uncharacteristically high car use and energy use compared to peer cities, especially since they have supportive urban densities and other factors that can underpin much less automobile dependence. Fundamental issues are raised about GM and LM and how to improve their transport sustainability. Policy implications with eleven recommendations are provided.
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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 | 0.009 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.008 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".