Questions about ‘Smart Growth: A critical appraisal of urban growth strategies in Australasian and North American Cities
Bibliographic record
Abstract
Smart Growth strategies are entrenched as planning instruments for sustainable urban growth in North American and Australasian cities. This involves preventing sprawl by establishing an urban limit, and encouraging higher density development at walking distances from public transport nodes and corridors. Questions are raised about the extent to which current practices are achieving the sustainable goals anticipated. Drawing on a larger research study undertaking a critical appraisal of smart growth strategies in seven Australasian and North America cities, this paper provides interim outcomes from Vancouver, Seattle and Portland. Key observations from the cities concerned are summarised under three overriding smart growth goals: establishing and maintaining urban growth boundaries and protecting of natural resources; delivering compact living environments and access to a variety of transport options; and creating viable communities who value urban lifestyles. Despite the criticism of smart growth, there is sufficient evidence to show that some positive outcomes are being achieved, and should contribute towards more sustainable urban form in the 21st century. Whether it is smart enough is another matter, and the development of practices and research will continue to play an important role. More fully recognising the significance of changing demographic profiles over the next decade and the different attitudes to urban lifestyles among the younger generations, should play a bigger part in future urban visions. Moreover, integrating and interrogating existing suburban development within urban growth strategies is argued to be an urgent, and potentially useful, new strategy. Also important is the development of evaluative tools to monitor progress in meeting smart growth objectives. What is certain is that simply allowing 20th century suburban sprawl to continue is not an option, and this is a message that all those involved in the political processes should understand.
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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 | 0.095 | 0.102 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.015 | 0.031 |
| Scholarly communication | 0.019 | 0.013 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.005 | 0.011 |
| Insufficient payload (model declined to judge) | 0.002 | 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".