Shrinking Communities Handbook A guide to developing strategies for shrinking communities in Canada
Bibliographic record
Abstract
"This report provides a foundation for the development of strategies to addressing urban shrinkage in Canadian communities, primarily small and medium-sized communities located outside of metropolitan areas. The purpose of this report is to enhance a common understanding of the issue of urban shrinkage in Canada, and to review approaches and strategies for Canadian communities to address the issue in a practical, responsive, and responsible manner. This is accomplished in three parts: first, an introduction to shrinkage; second, a discussion of approaches to addressing shrinkage; and third, the identification of guiding principles for the development of strategies in individual Canadian communities. The intended audience for this report are those people engaged in the process of planning for shrinking communities including elected officials, municipal staff, and members of the general public. As a concept shrinkage involves a number of components including population loss, as well as other economic, spatial, and political processes. It’s shaped by a number of factors including scale (i.e. whether the shrinkage is occurring at a local level only or a regional level) as well as by local context (i.e. history, geography, demography, governance, etc.). It’s cyclical and self-reinforcing nature are also hallmarks. With regard to the causes and effects of shrinkage, there are many. Globalisation, suburbanisation, political transition, declining birth rates, changes to the labour market, and environmental calamities are all noted causes of shrinkage, while building vacancies and abandonment, reduced economic output, reduced municipal revenues, and socio-economic decline are all noted effects. The issue of urban shrinkage has been steadily growing in Canada, adversely impacting communities located in rural and remote areas of the country, while evidence is growing that populated and industrialized areas of the country are beginning to show signs of shrinkage as well. While the issue has often been studied at an urban system’s level, the research on community-level solutions and strategies for Canadian communities has been lacking. In Canada, a country whose urban system has long been characterised by a dichotomy between the urban / industrial heartland and the rural / resource-based hinterland, government intervention in the form or regional development programmes was a key factor in keeping shrinkage at bay in the disadvantaged hinterland. However, the demise of regional development programmes by the mid 1980s, compounded with a shifting and globalising economy, and an aging population have created a polarised urban system. The nature of this system is key to understanding how to appropriately develop strategies and plans for shrinking communities. Approaches and strategies to address shrinkage fall into one of two categories. Firstly, growth-centric conventional approaches, such as culture-led regeneration schemes, land banks, tactical urbanism, and beautification programmes which attempt to restore a community to its ‘natural’ growing state. Secondly, are new approaches that seek to develop a new paradigm to address the issue, recognising that many communities will not practically return to a state of growth. These communities require alternative paths to address the challenges of shrinkage. These strategies include the use of green infrastructure, localism, and the concept of smart-decline or right-sizing to address the issue. To develop your own community’s strategy involves a synthesis of the research carried out in this report. To that end five guiding principles have been identified and discussed: • Being placed-based and context specific; • Taking the long view; • Integrating local and regional planning; • Being flexible and strategic; and, • Engaging and building consensus. These principles are intended to provide communities with a basis to develop their own strategy on a foundation of both research and practice. The report concludes with the identification of next steps in the process, namely the development of a planning framework to address the issue and a call to re-examine how we view shrinkage, not necessarily in terms of challenges and scarcity, but in terms of opportunities and surplus, as new approaches and alternative perspectives will be key to identifying workable solutions"@eng
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.011 |
| Science and technology studies | 0.018 | 0.003 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.072 | 0.009 |
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".