Balancing Concentration and Dispersion? Public Policy and Urban Structure in Toronto
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it