Being Realistic About Planning in No Growth: Challenges, Opportunities, and Foundations for a New Agenda in the Greater Sudbury, CMA
Bibliographic record
Abstract
Regional disparities, most notably of the 'heartland-periphery' pattern, have been a distinctive feature of Canadian urban geography throughout the industrial era. New regimes of economic prosperity, recessions, and restructuring in the post-industrial era coupled with demographic fluctuations have added new and accentuated divisions and disparities creating an increased gap between cities that are growing and not growing. Under these conditions, it seems realistic to expect that no-growth cities might begin to develop distinctive planning strategies centered on a theme of decline or no-growth scenarios. However, this has not been the case. The City of Greater Sudbury is located in North-eastern Ontario and is best known across Canada for its original resource-based ‘boom’, its unsustainable mining practices and subsequent decline. The 21st-Century City of Sudbury has since evolved into a more balanced regional centre. Nonetheless, the population of the City has been fluctuating over the last 30 years, experiencing decline, slow growth, and no-growth scenarios. \n\tThe first phase in the research establishes the documentary record of Sudbury’s decline alongside remedial initiatives undertaken at the federal, provincial, and local levels in the general attempt to kick start growth locally and remediate decline. The second phase in the research investigates how those involved in planning and economic development at the grassroots level deal with no growth through key informant interviews with planners, economic developers, consultants, and politicians. The research findings document the contradictory perceptions that surround planning in no-growth locales and further explore the challenges and opportunities associated with no growth urban areas. It concludes with a discussion of what might constitute alternative criteria for a new model of planning and development capable of generating more realistic economic and planning policy and strategy considerations for no growth urban areas and Northeastern Ontario.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.035 | 0.024 |
| Scholarly communication | 0.014 | 0.006 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.003 | 0.009 |
| Insufficient payload (model declined to judge) | 0.010 | 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".