Independent ageing, climate risks and automobile dependence in the Canadian prairies: Evidence from the Canadian Longitudinal Study on Aging
Bibliographic record
Abstract
• We study mobility in later life in the Canadian prairies. • We discover distinct leisure travel profiles among older residents. • We identify high car dependence and unequal risks to independent ageing. • We explain differential trends in travel choices and digital technology adoption. • We gauge the potential of emerging transport solution for sustainable and resilient mobility. We examine older adults’ leisure activities and mobility needs in one of the most automobile dependent regions in the Global North, the Canadian prairies. The motivation is to identify chances of independent ageing and sustainable travel as the region undergoes demographic transition while being subject to increased risk of extreme weather events that disrupt mobility. Using the Canadian Longitudinal Study on Ageing (CLSA), we identify six groups having different profiles of out-of-home leisure activities that seem to be essential to social connectedness and well-being. We analyse to which extent these practices depend on driving and digital technologies. We identify two groups who show strong signs of transport-related social exclusion. These groups do not benefit from digital technology and are subject to a dual mobility and digital divide that is exacerbated in the context of strong automobile dependence. Policy solutions need to be built on a holistic perspective involving municipal planning, digital skill development, transit funding and adaptation measures. Technological solutions, notably autonomous services, are likely to play a minor role in addressing pressing challenges in the Canadian prairies.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.010 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".