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Record W2729612135 · doi:10.55016/ojs/sppp.v9i1.42559

Mind the Gap: Transportation Challenges for Individuals Living with Autism Spectrum Disorder

2016· article· en· W2729612135 on OpenAlexaffabout
Carolyn Dudley, Jennifer Zwicker

Bibliographic record

VenueThe School of Public Policy Publications · 2016
Typearticle
Languageen
FieldEngineering
TopicTransportation and Mobility Innovations
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsAutism spectrum disorderPsychologySpectrum (functional analysis)PsychiatryAutismPhysics

Abstract

fetched live from OpenAlex

WHY IS THIS AN IMPORTANT ISSUE?An estimated 1 in 86 children are diagnosed with Autism Spectrum Disorder (ASD)1 making it the most commonly diagnosed childhood neurological condition in Canada.2 Transportation challenges for those with ASD are a growing issue in Canada. People living with ASD3 and others who live with neurodevelopmental disability (NDD)4 rely almost exclusively on public transit and caregivers for transportation. The current transportation options are insufcient in meeting the needs of this population. WHAT DOES THE RESEARCH TELL US?Transportation is essential to promoting quality of life The transit system plays an essential role in improving quality of life for individuals with ASD and for their caregivers. However, problems with cognition, perception and communication are barriers to independence in transportation. Availability of transportation is critical to enable high levels of physical activity among those with intellectual disabilities.5 Safe and reliable transportation improves one’s ability to participate in programs that support quality of life and impacts employment, volunteering, religious participation, exercise, self-advocacy and health care for people with intellectual and developmental disabilities.6 Caregivers for those with ASD emphasize that transportation is critical to enable meaningful opportunity and community engagement in employment, education, healthcare and social pursuits.7

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.956
Threshold uncertainty score0.342

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.035
GPT teacher head0.270
Teacher spread0.234 · 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 designTheoretical or conceptual
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

Citations3
Published2016
Admission routes2
Has abstractyes

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