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Record W1684453201

Assessing Transportation Disadvantage in Rural Ontario, Canada: A Case Study of Huron County

2015· article· en· W1684453201 on OpenAlexaffvenueabout
Eric Joseph Marr

Bibliographic record

VenueJournal of rural and community development · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicUrban Transport and Accessibility
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsDisadvantageDisadvantagedService (business)BusinessService providerGeographyEconomic growthSocioeconomicsPolitical scienceEconomicsMarketing
DOInot available

Abstract

fetched live from OpenAlex

In virtually all rural areas of Ontario, Canada, the limited availability of transportation alternatives means that rural residents without access to a personal vehicle are at an increased risk of transportation disadvantage. To date, little research has been conducted in Ontario as to the transportation limitations of rural residents, nor has a comprehensive study of groups at risk of transportation disadvantage been conducted. To address this gap, this research involved the development and testing of a transportation disadvantage framework, using Key Informant Interviews with service providers operating within Huron County, Ontario. Five demographic groups were found to be at risk of transportation disadvantage within Huron County: (1) older adults, (2) those with physical or mental disabilities, (3) youth, (4) people in low-income households, and (5) women. The results confirm that transportation disadvantage exists on a continuum, with some groups more disadvantaged than others, as well as some services more attainable than others. The framework was found to be a useful, and accessible, starting point for assessing groups at risk of transportation disadvantage in a rural community. The findings suggest that a coordinated transportation service, serving multiple demographic groups, may contribute to reducing transportation disadvantage while better utilizing the resources of existing service providers. Keywords: rural transportation; car dependence; transportation disadvantage; mobility; accessibility; rural Canada ------------------------------------------------- Resume Dans presque toutes les regions rurales de l'Ontario, au Canada, la disponibilite limitee des moyens de transport signifie que les residents ruraux n'ayant pas acces a un vehicule personnel ont des risques plus eleves d'etre desavantages en terme de transport. Jusqu'a present, peu de recherches ont ete menees en Ontario quant aux contraintes des transports des residents ruraux, pas plus qu'une etude detaillee n'a ete menee sur les groupes exposes a des risques de desavantage de transport. Afin d'aborder cet ecart, cette recherche a inclu le developpement et l'essai d'un cadre de desavantage de transport, en utilisant les entrevues aupres des informateurs cles avec les fournisseurs de service operant dans le comte de Huron, en Ontario. Cinq groupes demographiques ont ete consideres a risque de desavantage de transport dans le comte de Huron, en Ontario: (1) les adultes âges, (2) ceux ayant des incapacites physiques ou mentales, (3) les jeunes, (4) les personnes ayant un faible revenu et (5) les femmes. Les resultats confirment que les desavantages en terme de transport existent, sur un continuum, avec des groupes plus desavantages que d'autres, ainsi que des services plus atteignables que d'autres. Le cadre a ete reconnu comme utile et accessible; c'est un point de depart pour evaluer les groupes presentant des risques de desavantage de service de transport dans une communaute rurale. Les resultats suggerent qu'une coordination de service de transport, servant de multiples groupes demographiques, pourrait contribuer a reduire les desavantages en terme de transport tout en utilisant mieux les ressources des fournisseurs de service existant.

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)0.002
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 score0.227
Threshold uncertainty score0.423

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.051
GPT teacher head0.320
Teacher spread0.269 · 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

Citations17
Published2015
Admission routes3
Has abstractyes

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