The Politics of Post‐Suburban Densification in Canada and France
Bibliographic record
Abstract
Abstract This debate specifically focuses on densification as a particular dimension of (post‐) suburbanization. In the introduction, we discuss densification, along with ‘compactness' and ‘intensification', conceptual terms that have become buzzwords within urban planning. Objectives associated with these tend to be presented in the literature within a normative framework, structured by a critique of the negative effects attributed to sprawl. The perspective here is different. It is not normative but critical, and articulated around the analysis of political and social issues, related to the transformation of wider metropolitan space. Three main themes are developed: (1) the politics of densification (the environmental arguments favouring densification are highly plastic, and are thus often used to defend projects or initiatives which are actually determined by other agendas); (2) why morphology matters (a similar number of houses or square metres can be established in many different ways, and those different ways have political and social meaning); (3) the diversity of suburban densification regimes (it is not only the landscapes of the suburbs that are diverse, but also the local bodies governing them—between the small residential municipalities of the Paris periurbs and the large inner suburbs of Toronto lies a broad spectrum).
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.019 | 0.010 |
| Scholarly communication | 0.009 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".