Analysis of millennials and older adults’ automobility behavior in Hamilton, Ontario
Bibliographic record
Abstract
This study explores the automobility behavior of millennials (those born between 1980 and 2000) and older adults (65 years and older) and the factors that influence their automobility behavior using cross-sectional data from Hamilton, Ontario. This study focuses specifically on how automobility behavior of millennials and older adults is shaped by their socio-demographic characteristics, living arrangements, attitudes, and preferences toward transportation modes and residential location characteristics. Results from the binomial and ordinal logistic regressions suggest that depending on whether a millennial or older adult lives alone, with a partner, or in an apartment, their automobility behavior differs. The study also finds that positive attitudes and preferences toward sustainable travel behavior make both generations less auto-oriented, especially millennials. Regarding preferred residential location characteristics, compared to older adults, millennials’ preference toward off-street parking in their residential neighborhood is likely to influence their automobile use. Compared to older adults, living arrangements, attitudes, and preferences influence, to a greater extent, millennials’ attributes of automobility. Further, the study also suggests that living arrangements, attitudes, and preferences can differ among millennials and older adults. Consequently, the impact on each of the attributes of automobility behavior will differ.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".