Bibliographic record
Abstract
In this article, the author makes the case that urban planning is focused on the wrong issues, maintaining that increasing car use and automobile dependency is not a universal phenomenon linked to increasing incomes and wealth. Using the example of Zurich, the author describes why Zurich has much better sustainable transport outcomes than most cities can manage. Although Zurich is the wealthiest city in the world, walking, cycling and public transport experience high shares of the transportation load. Zurich’s successes are found to be not the result of bigger budgets or due to urban density explanations. The author describes the high level of management and organizational integration and co-ordination across all modes of transport in Zurich, noting that they are absent in Melbourne and Sydney. Rail transport service in Melbourne is used as a comparison to Zurich; results illuminate fragmentation and lack of co-ordination in rail transport services in Melbourne. These problems are exacerbated by the Australian fascination with privatization and public-private financing contracts. The author concludes with a discussion of the evidence around population density and the modal share of public transport in a number of world cities, finding that density is not a convincing explanation for the differences in public transport performance in these cities. One table gives the census figures for density and the share of work trips made by “sustainable‟ modes (public transport, walking and cycling) from the most recent census in here countries (2006 for Australia and Canada; 2000 for the United States). This article was previously published in D!SSENT Number 32, Autumn/Winter 2010.
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 | 0.010 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.011 | 0.018 |
| Scholarly communication | 0.014 | 0.018 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.009 | 0.012 |
| Insufficient payload (model declined to judge) | 0.005 | 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".