Intensification and Sprawl: Residential Density Trajectories in Canada's Largest Metropolitan Regions
Bibliographic record
Abstract
This study investigates the balance between forces of standardization and differentiation in the evolution of residential density in Canada's four largest metropolitan regions between 1971 and 2006. The leading factors of standardized development are the continentwide postwar adaptation of urban form to the automobile and growing housing space consumption. The influence of these factors is manifested in increasing convergence in the density levels of the four metropolitan regions as one moves from older to newer zones. Nonetheless, inherited urban forms, topography, economic and demographic performance, and land-use and transportation policies all have the potential to shape distinct density patterns. Each metropolitan region presents a specific density trajectory: Toronto registers a pattern that can be qualified as stable and recentralized; Montreal emerges as a decentralizing metropolitan region; Vancouver shows clear signs of intensification; and in Ottawa-Hull the trajectory combines decentralization and stability. These different metropolitan trajectories offer lessons for intensification strategies. Findings suggest that continentwide tendencies are shaped by features specific to each metropolitan region, and that successful intensification policies must build on those features.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".