Bibliographic record
Abstract
The Pedestrian and the City provides an overview and insight into the development, politics and policies on walking and pedestrians: it includes the evolution of pedestrian-friendly housing estates in the 19th century up to the present day. Key issues addressed include the struggle of pedestrianization in town centers, the attempts to create independent pedestrian footpaths and the popularity of traffic calming as a powerful policy for reducing pedestrian accidents. Hass-Klau also covers the wider aspects of urban and transport planning, especially public transport, essential for promoting a pedestrian-friendly environment. The book includes pedestrian-friendly policies and guidelines from a number of European countries and includes case studies from the UK, Germany, Britain, France, Spain, Italy, the Netherlands, Denmark, the US and Canada, with further examples from ten additional countries. It also contains a unique collection of original photographs; including ‘before’ and ‘after’ photos of newly introduced pedestrian-friendly transport policies. As the pedestrian environment has become ever more crucial for the future of our cities, the book will be invaluable to students and practicing planners, geographers, transport engineers and local government officers.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.027 | 0.009 |
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".