Challenging the myth of apocalyptic aging at the local level of governance in Ontario
Bibliographic record
Abstract
This article contributes to the literature on population aging and community development by exploring whether decision‐makers at local levels of governance in Canada subscribe to an age‐friendly or apocalyptic demography view of their older populations. Drawing on a qualitative analysis of six medium‐sized cities in Ontario, we capture the views of local leaders in different economic, demographic, and geographical contexts to understand the challenges, implications, and opportunities for community development as their populations age. We chose to capture the regional differences that exist between two rapidly growing suburban municipalities of the Greater Toronto Area and four cities in northern, eastern, and southwestern Ontario that face the dual challenges of industrial restructuring and an aging population. We also provide a case study involving two of these cities where local leaders actively encourage the in‐migration of seniors from other communities as a strategy for economic development and growth. The findings suggest that local leaders across Ontario generally embrace an age‐friendly view, but acknowledge there will be challenges to meeting the needs of a large older population, especially with regards to health and long‐term care.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.026 | 0.031 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".