Mobility of Canadian Elderly: Multilevel Analysis of Distance Traveled in the Hamilton Census Metropolitan Area, Ontario, Canada
Bibliographic record
Abstract
The general objective of this study is to determine individual and neighborhood characteristics that affect distance traveled and the variability of these factors on each mode type using multilevel analysis. It hopes to contribute to the general discussion on land use-travel links with reference to promoting communities facilitating healthy aging while further building up the GIS-based decision support system for evaluating the impact of demographic change and socio-economic policies in the study area. This paper highlighted the general decline in the trip frequency, length, and duration as age advances and displayed that the gender divide tends to vanish among the elderly. But men strive to drive as long as possible while women tend to become car, bus and taxi passengers when they get older. The results suggest the expansion of mobility choices for the elderly upon driving cessation and for these to be gender sensitive. Multilevel analysis showed that while neighborhood attributes such as location and land use mix are important, the great majority of the variation in distance traveled can be explained at the level of the individual than it is by the differences between neighborhoods in the area. The implication is that, land use policies would find greater weight when the issue being addressed by policy is towards the encouragement of the use of non-car modes over that of private vehicles. If not, policy efforts should be directed at the level of individual behavior, e.g. pricing, tax regulations, etc.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.004 | 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".