Downtowns that Work: Lessons from Toronto and Chicago
Bibliographic record
Abstract
Among downtowns of North American metropolitan regions, two have performed especially well in terms of the presence of employment, residential development and diversity of land uses over the last decades: those of Toronto and Chicago. This paper concentrates on the factors responsible for their success. It reviews the history of the two downtowns since World-War-II, giving special attention to the capacity ‘macro-decisions’ have of creating path dependencies. Identifi ed macro-decisions include strategic investments in downtown-focussed public transit and improvements to the diversity and amenities of the downtowns. Th ere are important differences in the approaches taken in the two downtowns. Th ese relate in part to organizational specifi cities. If in Toronto institutional structures and political coalitions play a major role in explaining the adoption of policies favourable to the downtown, in Chicago it is the priorities of powerful mayors that loom largest. The paper proposes a multicausal model, which shows how numerous decisions of diff erent nature, along with their interactions and consequences, have contributed to positive downtown outcomes in the two cities. The main lesson from the two cases is that downtown success cannot be improvised as it is the outcome of long chains of policies, which interact positively with market trends, favouring core areas.
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How this classification was reachedexpand
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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 teacher head, 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".