An Evaluation of the Universal Accessibility of Bus Stop Environments by Senior Tourists
Bibliographic record
Abstract
Sustainable mobility demands an integrated approach covering all modes of transport in a built environment designed for everyone.Social inclusion strategies required the improvement of transportation for people with reduced mobility.Universal accessibility has been incorporated into urban renovation processes, settlement, housing and transportation.Assessments have been made in measuring the performance of spatial indicators and usually consider technical parameters and/or user perception.In the context of accessible tourism, infrastructures and services have been adapted to be inclusive for all.Accessible built environments are required hence urban spaces, buildings, transport vehicles, information technology & communication, and services must bear in mind the approach of Age Sensitive Design.The research project Accessibility for All in Tourism focuses on bus stops designed to be age-friendly and inclusive.A questionnaire was developed for the elderly tourist aged 60+ about their perceptions of bus stop environments in their countries.Findings indicate that elderly tourists with disabilities are more critical of the existing accessibility conditions, and have a greater perception of the inclusive characteristics of bus stops.Furthermore, although older people take barrier-free spaces into account, there is some criticism around pedestrian crossings, bench design and the lack of room for wheelchair users.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".