Engaging Citizens in Sustainable Development Policy in Regional Planning: A Comparative Study of the Regional Municipalities of York (Ontario) and Wood Buffalo (Alberta)
Bibliographic record
Abstract
This paper explores whether changes in direct settlement patterns by recent visible minority immigrants influence the development and implementation of sustainability planning policy—the Integrated Community Sustainability Plan (ICSP)—for two regional municipalities in Canada—York (Ontario) and Wood Buffalo (Alberta). Since 2005, having ICSPs has been required in Canada; furthermore, it has become a well-documented fact that Canada's current population growth is largely attributed to migration by ethnic visible minority immigrants. While historically, immigrants settled in traditional urban areas (i.e. Montreal, Toronto, Vancouver), recent immigrants are increasingly directly settling in suburban regions. As such, sustainability and sustainable development are the site of policy and politics at which this study will examine public engagement and consultation practices of the two regional municipalities, in regards to their changing social composition. Specifically, this study is interested in whether there has been culturally appropriate and adequate response by the two regional municipalities to the change in social composition that has occurred through migration by recent visible ethnic minority immigrants in terms of public engagement and consultation in the development and implementation process of their respective ICSPs.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.016 | 0.005 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".