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 suburban older adults and a synthesis of the challenges and opportunities of aging in suburban environments. This chapter serves to provide (1) a snapshot of Canadian suburban demographic trends, (2) an overview of the state-of-the-art thinking on suburban aging, and (3) contextual framing for the in-depth research chapters and vignettes that make up the suburban part of this book. Canada’s built environment and population growth predominantly occurs on the (sometimes sprawling) urban fringe. Put simply, Canada is a suburban nation. In Canada’s largest metropolitan areas, including Vancouver, Montréal, and Toronto, the proportion of suburban residents exceeds 80% (Gordon and Janzen, 2013). Generally, traditional forms of suburban locations can be characterized by a variety of factors including the proportion of single-family housing, car commuting patterns, population density, and home-ownership rates. However, we recognize that the modern suburban landscape is complex and diverse (Keil, 2017) and that there is no single perfect operational definition of suburban (Forsyth, 2012). We adopt the following operational definition in order to provide a generalized overview of suburban demographic trends in Canada: suburban areas are dissemination areas (as defined by Statistics Canada) with a population density between 1,000 and 4,000 people per square kilometre with over 60% of commutes made by car, or simply with a population density of 400–1,000 people per square kilometre (Channer et al, 2020). Using the data from the Statistics Canada (2019a) population estimates, we found that more than half (18 million) of Canada’s population resides in suburban areas.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.011 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.024 | 0.002 |
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".