Questioning density : an appraisal of the theoretical and empirical basis for smart growth in the Toronto Census Metropolitan Area
Bibliographic record
Abstract
Smart Growth-densification is an essential element of the local planning ethic. However, little research has been undertaken on impacts of densification in the Toronto Census Metropolitan Area (CMA). Accordingly, the first component of this MRP presents a critique of Smart Growth theory that is divided into two strands. The first strand identifies four methodological limitations in the foundational density research upon which Smart Growth theory is based. The second strand concludes that much of reviewed density research has been over-interpreted and appropriated to serve the Smart Growth rationale. To appraise the empirical basis for Smart Growth in the Toronto CMA, four hypotheses are tested using a cross-sectional and quasi-longitudinal design. Although 2006 census tract (CT) density and CT densification (1986-2006) demonstrated a relationship to sustainable outcomes, the nature of these did not conform to predictions of Smart Growth theory. The study also indicated that the relationship between density and outcomes was largely non-linear and partially attenuated by household-level factors. When all sections of this MRP are taken into account, the basis for Smart Growth-densification, according to its present definition, appears increasingly tenuous.
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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 | 0.008 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.003 | 0.024 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".