MétaCan
Menu
Back to cohort
Record W4362622293 · doi:10.1080/21650020.2023.2197979

Analysis of millennials and older adults’ automobility behavior in Hamilton, Ontario

2023· article· en· W4362622293 on OpenAlexafffundabout
Shaila Jamal, K. Bruce Newbold, Darren M. Scott

Bibliographic record

VenueUrban Planning and Transport Research · 2023
Typearticle
Languageen
FieldEngineering
TopicTransportation and Mobility Innovations
Canadian institutionsMcMaster University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsGerontologySociologyPsychologyMedicine

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.021
Threshold uncertainty score0.991

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.045
GPT teacher head0.326
Teacher spread0.281 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2023
Admission routes3
Has abstractyes

Explore more

Same venueUrban Planning and Transport ResearchSame topicTransportation and Mobility InnovationsFrench-language works237,207