Spaces for Aging in Place & Downtown Revitalization: The Experiences of Downtown Living for Older Adults in Calgary, AB
Bibliographic record
Abstract
Canada is undergoing a significant demographic shift, with older adults aged 65 and above becoming one of the fastest-growing groups at approximately 20% of Canada’s overall population. Generally, research on age-friendly planning and aging in place emphasizes areas where older adults are larger proportions of municipal populations. Moreover, aging in place research in Canada traditionally focuses on suburban communities. However, recent census data suggests older adults are increasingly choosing to live in urban downtowns. This shift raises critical question about what attracts older adults to downtown living, and whether municipalities are adequately prepared to support them in a “youthified” urban structure. The purpose of this report is to explore the intersection of age-friendly planning, aging in place, and state-led downtown revitalization policies, using Calgary, Alberta as a case study in the post-pandemic planning era. As Canada’s demographically youngest and fastest growing province, Alberta has marketed itself as a “youthful” space to draw working age adults. However, this preference risks overlooking planning for older adults presently aging in Albertan communities. Calgary is one of Canada’s fastest growing cities, with council-directed downtown revitalization policies intended to enhance vibrancy and livability amid concerns associated with Canada’s highest office vacancy rates. However, it remains unclear how these state-led revitalization policies address the needs and desires of older adults already living in Calgary’s main downtown neighbourhoods: the Beltline, Chinatown, East Village, Eau Claire, and Downtown West.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.023 | 0.009 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.004 |
| 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".