Bibliographic record
Abstract
Although neither of us knew it at the time, my friend Celia Moore set this book in motion over a decade ago when she handed me a dog-eared copy of The Myth of the North American City and said, "I think this is your kind of thing."It was.Born in Canada to American parents, and now married to an American émigrée, I have spent most of my life traversing the world's longest undefended border, wondering how these two societies and their cities could be so similar and yet so different.As a trained urban planner attuned to the physical and spatial order of cities, I have come to understand that Canadian and American urban built environments differ in important respects, and that these differences have important social, economic, environmental, and even political consequences.As a student of politics and history, I questioned whether these differences are the direct outcomes of divergent political cultures and patterns of social conflict, or are reducible to economic forces.This book investigates how physical environments are shaped by the ways in which urban development policy decisions are made -in essence, how contention among interests, and the ideas they champion, is organized by political institutions.I hope that as a result of this work we know more than we did before, and can ask new questions.This book began as a doctoral dissertation at the University of Toronto.I am grateful for the support of my committee, my supervisor David Wolfe and committee members Phil Triadafilopoulos and André Sorensen, each of whom provided valuable insights
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.333 | 0.202 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".