Bibliographic record
Abstract
This book would not have happened without the support of the Neptis Foundation.Over a decade ago, Neptis commissioned my short history of the Toronto region's piped infrastructure -the initial spark for the whole endeavour -and subsequently funded my research on the history of regional planning, the seed from which this book grew.Its generous financial support for those two publications allowed me to find my bearings in a new intellectual field, develop original observations, and establish connections with a community of local planners and urbanists that continue to this day.I am thus grateful to both the founder, Martha Shuttleworth, and the founding executive director, Tony Coombes, who sadly has not lived to see its final completion, for their support.I would also like to thank the interviewees who took time to offer me their recollections and reflections.Interviews were an essential part of this research, providing details I could not have found elsewhere but, perhaps more importantly, perspective and insights that allowed me to develop thoughts of my own.Everyone I interviewed, planners and politicians, from the most senior to the most junior, was sincere and helpful.I remain especially grateful to Len Gertler (who died in 2005) and Eli Comay
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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 | 0.003 | 0.014 |
| 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 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.333 | 0.209 |
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".