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Record W1481310016

Policy Areas Impinging on Elderly Transportation Mobility: An Explanation with Ontario, Canada as Example

2007· article· en· W1481310016 on OpenAlexaffabout
Rubén Mercado, Antonio Páez, K. Bruce Newbold

Bibliographic record

VenueSocial and Economic Dimensions of an Aging Population Research Papers · 2007
Typearticle
Languageen
FieldSocial Sciences
TopicUrban Transport and Accessibility
Canadian institutionsMcMaster University
Fundersnot available
KeywordsSustainabilityStock (firearms)BusinessTransport policyTransportation planningPublic economicsPopulationRegional scienceEconomic growthEnvironmental planningTransport engineeringPublic transportEconomicsGeographyEngineeringMedicine
DOInot available

Abstract

fetched live from OpenAlex

As countries face the challenges posed by rising numbers of older persons, the need to reassess their respective policies to address transport needs in aging societies is increasingly recognized in relation to health and sustainability goals. This paper proposes the examination of six interrelated policy areas affecting elderly mobility in a country or administrative region. A general survey of policy developments in each of these areas could improve current strategies and existing processes in the planning and implementation of mobility services that will be responsive to both elderly and the general population now and in the future. These include: 1) general transport policy framework; 2) travel mode preference; 3) alternative transport infrastructure stock and investments; 4) housing-land-use-transportation linkage; 5) research and technology applications that improve travel mode and environment; and 6) institutional and legal reforms. These policy areas are discussed and given concrete elucidation in the case of Ontario, Canada. Reflections and recommendations for further research and policy action deemed critical in the case region are highlighted.

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 score0.066
Threshold uncertainty score0.886

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.050
GPT teacher head0.358
Teacher spread0.307 · 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

Citations1
Published2007
Admission routes2
Has abstractyes

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