Bibliographic record
Abstract
With a landmass of approximately 7000 square kilometres and a population of roughly five million, the Greater Toronto Area is Canada's largest metropolitan centre. How did a small nineteenth-century colonial capital become this sprawling urban giant, and how did government policies shape the contours of its landscape?In Toronto Sprawls, Lawrence Solomon examines the great migration from farms to the city that occurred in the last half of the nineteenth century. During this period, a disproportionate number of single women came to Toronto while, at the same time, immigration from abroad was swelling the city's urban boundaries. Labour unions were increasingly successful in recruiting urban workers in these years. Governments responded to these perceived threats with a series of policies designed to foster order. To promote single family dwellings conducive to the traditional family, buildings in high-density areas were razed and apartment buildings banned. To discourage returning First World War veterans from settling in cities, the government offered grants to spur rural settlement. These policies and others dispersed the city's population and promoted sprawl.An illuminating read, Toronto Sprawls makes a convincing case that urban sprawl in Toronto was caused not by market forces, but rather by policies and programs designed to disperse Toronto's urban population
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.035 | 0.004 |
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".