MétaCan
Menu
Back to cohort
Record W4235716459 · doi:10.32920/ryerson.14668200

Assessing senior perspectives' towards ride for free public transportation: a case study in Oakville, Ontario

2021· preprint· en· W4235716459 on OpenAlexaffabout
Stephanie J. Mah

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldHealth Professions
TopicOlder Adults Driving Studies
Canadian institutionsToronto Metropolitan UniversityUniversity of Alberta
Fundersnot available
KeywordsPublic transportTravel behaviorTransport engineeringBusinessMarketingAdvertisingEngineering

Abstract

fetched live from OpenAlex

This research investigated the Ride for Free Public Transportation program for seniors in Oakville, Canada. Using a mixed-methods approach, participants were surveyed (n=131) to understand their travel behaviour, and interviewed (n=16) to understand their perspectives towards taking public transportation. While 63% of seniors said that the Ride for Free Transit Program did not impact their travel behaviour, 37% said that it increased their public transit use. The most popular reason for seniors to use public transportation was taking it by themselves. Some interview respondents said that they used public transportation because they would not have to ask others for rides or they did not have access to a car. Seniors suggested that more education of how to use the bus and transfer could increase senior ridership. This research may aid other municipalities considering similar programs, which could help to sustain the independent mobility of seniors.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.052
Threshold uncertainty score0.202

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0120.002
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.180
GPT teacher head0.469
Teacher spread0.289 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations0
Published2021
Admission routes2
Has abstractyes

Explore more

Same topicOlder Adults Driving StudiesFrench-language works237,207