Bibliographic record
Abstract
this book on the history of Toronto planning began with what might be called a layman's curiosity.When the subject first entered my mind in the late 1990s, I knew Toronto reasonably well for I had been living in the city for over ten years and visiting friends and family there for many more years than that.But I was no expert in its history or physical form, and by no means was I a Toronto aficionado.Nor was I what academics call an "urbanist," the evolution of cities having been entirely absent from my academic training in history.Yet as I walked and drove the city streets I began to observe, or perhaps more correctly to sense, the ineffable complexity of human endeavour that such a big, old city was, and I found myself wondering, as a historian, how and why its physical form came to be.Why were all those buildings, streets, parks, industrial plants, and shopping areas where they were and as they were?Who or what made them so and by what means?Why such questions began pervading my thoughts, I cannot say.Nothing in my background led me to ask them -let alone equipped me to answer them.Perhaps I had been just breathing the local air, for matters such as the merits of residential densities and the complexities of the land-use/transportation nexus seem to be a part of everyday discourse in Toronto, even among those who know little about them.Whatever the reason, I was hooked.Fortunately, I had friends and colleagues who could offer guidance, and I soon embarked on a program of historical research for the Neptis Foundation
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.539 | 0.259 |
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".