Growth Management and Affordable Housing in Greater Toronto
Bibliographic record
Abstract
(CMHC) under the terms of the External Research Program, but the views expressed are the personal views of the author and do not represent the official views of CMHC. Growth Management and Affordable Housing in Greater Toronto ♦ Greg Suttor 2007 ♦ iiiAcknowledgements and Disclaimer Housing policy needs its feet in social policy and urban development policy, and clear eyes on the housing and labour markets. This study attempts to connect such dots in Greater Toronto. Perhaps in time such efforts can help nudge housing policy beyond today’s ambivalent regime. These themes are enduring interests of mine, threads from earlier work on the 1996 housing needs study and the 1998-99 homelessness report. I greatly appreciate the opportunity CMHC’s External Research Program has given me to do this study. I thank David Hulchanski and Bob Murdie for encouraging my research interests generally, and David for my association with the Centre for Urban and Community Studies (and the U of T libraries!). Peter Pathinather and David Lou of Statistics Canada facilitated the custom census data; Doug Pollard was helpful as CMHC liaison. Richard Maaranen of CUCS did the wonderful maps. This report benefited from Blair Badcock’s comments on a draft of Parts 1 to 5. The study’s errors and shortcomings are mine. This report is unrelated to my employment at the City of Toronto and was prepared on my own time. The views and analysis presented here are mine and are not those of the City of Toronto.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 0.001 |
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".