Are Baby Boomers Likely to Retire to the City in Canada
Bibliographic record
Abstract
Due to the size of the baby-boomer generation, the question of where they will retire has important transportation planning implications. If they are to remain in the suburbs in retirement, this will have very different consequences than if they move to urban, transit-rich neighbourhoods – a possibility that has been raised frequently in recent years. This paper addresses the issue by looking at movers from Canadian microcensus data over 20 years and 4 censuses for Canada’s six largest cities. While concentrating on Canadian cities, the paper develops a robust approach to evaluate the evolution of where retirees (or any age-group) have been moving, and how these trends might continue into the future. It does so by introducing a continuous Urban Core Index to classify census tracts as being part of the “Urban Core” or not. Then, disaggregate data on movers for the censuses of 1991, 1996, 2001 and 2006 are analysed in three phases. First they are analysed graphically, then with a trend analysis and finally through the use of logistic regression. Logistic regression models are used to compare the evolution of the effect that being over 65 has on the odds of choosing to live in the “Urban Core.” Clear trends of 65+ movers increasingly moving to the suburbs are observed for three of the cities (Montreal, Calgary and Edmonton). For the other three cities such an increasing trend of choosing to move to the suburbs is not so clearly observed. At the same time and based on the observed trends, it does not appear that future retirees (and namely the baby boomers) are about to change previous patterns and move increasingly to the city.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".