Balancing Concentration and Dispersion? Public Policy and Urban Structure in Toronto
Bibliographic record
Abstract
By North American standards Toronto is a concentrated agglomeration. Its downtown has enjoyed spectacular growth since the 1960s; most inner-city neighbourhoods are perceived as desirable; and public transit patronage is high relative to that of same-size North American metropolitan regions. Still, it is within dispersed, car-oriented, suburbs that most post-1950 development has taken place. This agglomeration is composed of two realms—a concentrated and a dispersed realm—differentiated by their respective land-use-transportation dynamic. The concentrated realm is defined by a considerable reliance on walking and public transportation, a mixing of land uses and overall higher employment and residential densities than elsewhere in the metropolitan region. Meanwhile, the dispersed realm is car dependent, dominated by large monofunctional zones and developed at a relatively low density. The author links the coexistence and respective importance of these two realms in the Toronto agglomeration both to the nature of urban policies implemented since 1950 and to the circumstances that have led to their adoption. The construction of expressways, suburban type land-use planning, and a generous provision of open space have abetted dispersion. By contrast, the construction of a subway system and measures encouraging the redevelopment of underused land have promoted growth within the concentrated portion of the agglomeration. It is noteworthy, however, that these measures have failed in their attempts to induce concentration beyond the prewar urbanized perimeter. The author examines the positive and negative aspects of the presence of these two realms within a given agglomeration and highlights the threat newly adopted policies represent for the concentrated realm.
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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 | 0.008 | 0.010 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".