Bibliographic record
Abstract
In this overview chapter, we call upon data from Statistics Canada and the academic literature to present some stylized facts and figures regarding urban older adults and a synthesis of the challenges and opportunities of aging in urban environments. This chapter serves to provide (1) a snapshot of Canadian urban demographic trends, (2) an overview of the state-of-the-art thinking on urban aging, and (3) contextual framing for the in-depth research chapters and vignettes that make up the urban part of this book. Canada is predominantly a nation of rural spaces. By land area, urban locations occupy only 0.25% of Canada’s 9.9 million square kilometres. However, urbanization is quickly changing the national landscape. While Canada’s urban areas are growing steadily, they are simultaneously driving considerable suburban growth in their periphery. As we note in Chapter 6, Canada is a suburban nation. And those huge suburbs are growing around Canada’s urban centres. The three largest metropolitan areas (which include both urban and suburban areas), Toronto, Montréal, and Vancouver, are home to more than a third of all Canadians, with a combined population of 12.5 million (Statistics Canada, 2019). For many, urban Canada evokes images of these three iconic cities. Big, bustling conurbations with dense downtowns, skyscrapers, and expensive housing. But like suburban and rural areas, urban regions can take a variety of shapes and forms. Although there is no one perfect definition of ‘urban’, we adopt the following operational definition in order to provide a generalized overview of urban demographic trends in Canada: urban areas are dissemination areas (as defined by Statistics Canada) with a population density of 5,000 or more people per square kilometre, or areas with a population density of 1,000 to 5,000 people per square kilometre where fewer than 60% of population commutes by car (Channer et al, 2020).
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.015 | 0.002 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.029 | 0.004 |
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".