Attitudes toward Urban Living, Landscape, and Growth at the Dawn of Greater Toronto's Growth Management Era
Bibliographic record
Abstract
any required final revisions, as accepted by my examiners. I understand that my thesis may be made electronically available to the public. ii The Greater Toronto Area (GTA) is Canada’s largest metropolitan area and principal destination for international migration and investment. Over the next 25 years, the GTA is anticipated to grow by approximately 2.5 million people to a population of almost 8 million. While many view this growth as a symbol of economic prosperity, others see it as a threat to Toronto’s economic, environmental and social well-being due to the dispersed, automobile-oriented way in which the city has accommodated its growth since the 1950s. Over the last two decades, planners have focused much energy on ameliorating the shortcomings of post World War II urbanization by developing policy measures such as Smart Growth, Growth Management, and New Urbanism that aim to alter the way in which cities are built and thereby effect change in the lifestyles that have precipitated from this landscape. In Ontario, the Provincial Government recently launched a Growth Management campaign for the Toronto area called Places
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".