Characterising Transit Oriented Development in the Paris metropolitan region: what type of TOD are they?
Bibliographic record
Abstract
Transit Oriented Development (TOD) is a planning model that was introduced by Calthorpe (1993) in the United States. However, it has been applied in different international contexts: in high density urban environments (Asia) but also in medium density urban environments like in Australian cities (Perth, Brisbane) and in Canadian cities (Vancouver). The TOD concept is understudied in Europe. In this paper we analyse TOD-like projects using TOD criteria measurement – like the density of the built environment, the quality of public spaces, accessibility to public transport, the mix of land-uses – to determine the kind of TODs they are. Those criteria have been selected according to the literature on the topic. For this analysis of TOD projects, we use geospatial data for the Paris metropolitan region. After the identification of typical Transit Oriented projects, the objective is to analyse recent TOD case studies (e.g., ZAC Pleyel project in St-Denis). The aim is to understand why some criteria are more challenging than others to implement in practice. The qualitative data has been collected through semi-structured interviews with urban stakeholders. The overall aim of the paper is to provide planning recommendations for best practices of TODs in a high density environment such as the Paris metropolitan region.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".