Bibliographic record
Abstract
The suite of policies known as the 'compact city' has emerged as the most popular prescription for reducing automobile dependence. The evidentiary base for the compact city draws on previous studies that compared population density and automobile use in a range of metropolitan areas, and that concluded that density is strongly related to automobile use. This paper re-examines the data on transport and density in US, Canadian and Australian cities, using census data on mode share for the journey to work, and on the density of 'urbanised areas'. This comparison is possible because the three countries' census agencies collect density and mode share data on a comparable basis, although the Australian and Canadian agencies only publicly released density data following their 2006 censuses. These data allow cross-city and cross-national comparisons to be performed on a more accurate basis than was possible at the time of the earlier studies. Standard statistical techniques are used to examine the relationship between density and the mode share for automobiles, public transport and walking/cycling. The relationship turns out to be different from that reported in previous studies: public transport and automobile use are only weakly correlated with density, while walking and cycling show no correlation at all. The significance of these results is discussed in light of equivalent British and European density and travel data, and some figures on centralisation of employment. The conclusion is that the effectiveness of the compact city model has been overstated, and the effectiveness of transport policy itself understated.
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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 | 0.004 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.021 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".