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 \nregion, now known as the Greater Golden Horseshoe (GGH). To carry out and monitor its \npolicies, the Province needs reliable ways of measuring density and monitoring how it changes \nover time. However, definitions of density vary and there are many approaches to its measurement. \nThis paper reviews common definitions and discusses methodological and data problems associated \nwith density measurements in the GGH. The authors examine existing density distributions \nin the GGH using 2001 census data at the scale of municipal areas, census tracts, and \ncensus dissemination areas, and analyse 10 sample census tracts in Urban Growth Centres to \ncompare gross and net densities for different types of development areas in the GGH. Detailed \nprofiles are provided for five of those tracts. The authors note problems with using gross density \nfor making comparisons between areas or time periods, and problems with using census data in \ndensity calculations. \nConsistent, region-wide definitions and data are needed to develop a detailed understanding of \nexisting trends in population and jobs density, land use, development patterns, and housing issues. \nThe authors recommend the delineation of small census tracts with permanent boundaries \nfor the area of the GGH that is expected to build up during the next 20 to 30 years, as well \nas the creation of a regional database on employment location, density, and output. They also \nurge the government to make parcel data, or a comparable database, available to researchers \nand policy analysts.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".