Urban Density in the Greater Golden Horseshoe
Bibliographic record
Abstract
The Ontario government has recently taken a proactive approach to growth planning in the Toronto region, now known as the Greater Golden Horseshoe (GGH). To carry out and monitor its policies, the Province needs reliable ways of measuring density and monitoring how it changes over time. However, definitions of density vary and there are many approaches to its measurement. This paper reviews common definitions and discusses methodological and data problems associated with density measurements in the GGH. The authors examine existing density distributions in the GGH using 2001 census data at the scale of municipal areas, census tracts, and census dissemination areas, and analyse 10 sample census tracts in Urban Growth Centres to compare gross and net densities for different types of development areas in the GGH. Detailed profiles are provided for five of those tracts. The authors note problems with using gross density for making comparisons between areas or time periods, and problems with using census data in density calculations. Consistent, region-wide definitions and data are needed to develop a detailed understanding of existing trends in population and jobs density, land use, development patterns, and housing issues. The authors recommend the delineation of small census tracts with permanent boundaries for the area of the GGH that is expected to build up during the next 20 to 30 years, as well as the creation of a regional database on employment location, density, and output. They also urge the government to make parcel data, or a comparable database, available to researchers and policy analysts.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".