Still Dispersed and Decentralized? Evaluating the Influence and Implementation of Smart Growth in Mid-Sized Canadian Cities
Bibliographic record
Abstract
Since the late 1990s, the Smart Growth agenda has found broad acceptance within Canadian planning circles as a framework for sustainable development. Smart Growth emerged following decades of dispersed and decentralized growth that dominated urbanization across North America following WWII. Although Smart Growth and its policies for growth management have become the normative approach within the planning discipline, there is little evidence to suggest its actual impact on growth patterns in mid-sized Canadian cities. Through a series of spatial analysis methods, this research examines whether the emergence of Smart Growth has influenced growth patterns of four mid-sized Canadian Census Metropolitan Areas from 1990 to 2010. Findings indicate growth patterns as mixed and limited through the lens of Smart Growth, as growth does not mirror a complete shift consistent with the Smart Growth paradigm. While there is some indication of change, the findings of this research are not enough to overturn the profile of mid-sized Canadian cities as dispersed and decentralized. Subsequently, growth patterns were considered relative to inputs adopted at the respective regional and municipal levels to ascertain the extent to which planning has successfully achieved its objectives. This approach adopts an ex post facto conformance-based plan evaluation, whereby successful implementation is based on conformance between growth patterns and adopted inputs. The findings of this research indicate that while adopted inputs across each case mirror the language of Smart Growth, growth patterns indicate more limited conformance. These findings indicate further evidence of the challenges to disrupting entrenched patterns of urbanization and the disconnect between plans and outcomes.
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".