Bibliographic record
Abstract
With a greater regional population of eight million residents, Toronto is one of the largest and fastest growing metropolitan areas in North America and Canada’s emergent global city. Toronto is both exceptional and typical in its continental context. Originally an indigenous peoples’ crossroads, a British military garrison and town were established at Toronto in the 1790s and a municipal government was incorporated in 1834. Its 19th- and early-20th-century history of railway and industrial development, and also European immigration, is similar to other cities of similar vintage. While geographically proximate to Midwestern “rustbelt” cities, Toronto evaded their postwar fate; indeed, the region’s growth profile and diversified manufacturing, services, and agricultural economy more closely resembles that of postwar boomtowns in the American New South and Southwest than formerly dynamic, but now struggling, Great Lakes cities. Once smaller and economically and culturally secondary to Montreal within Canada, greater Toronto is now the country’s largest metropolitan area, predominant economic engine, and English-speaking cultural center. Toronto is further distinguished from many American cities in having become a hyper-diverse attractor of immigrants since the 1980s. A predominately white and British city as recently as 1970 (indeed, author Wyndham Lewis characterized Toronto as a “mournful Scottish version of an American city” in 1940), more than half the population of both the central city and the broader region is now foreign born and nonwhite. Toronto is thus a globally significant crucial case in urban immigrant integration and social cohesion. Academic scholarship on Toronto lacks comprehensive coverage and an identifiable progression of schools of thought. Most works focus on illuminating specific historical events or social and economic phenomena rather than situating the city and its development in a broader context. Nonetheless, these works cumulate to illustrate Toronto’s contemporary and historical relevance to urban studies. This article generally excludes comparative works in which Toronto is one of several cases unless the treatment stands on its own. It also excludes works that focus primarily on provincial-level matters, although it should be acknowledged that the Province of Ontario plays a much greater role in the municipal sphere than do other Canadian provincial and American state governments.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.669 | 0.343 |
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".