Accessibility at Airports: How Digital Media can put Toronto at the Forefront for Accessible Airport Travel
Bibliographic record
Abstract
This major research project explores the potential function of a mobile application to organize the consignment of wheelchairs at airports. Right now, many airports around the world are struggling to deal with the influx of wheelchair passengers. Many airports do not utilize any digital technology tools to assist them in the process of providing wheelchair service. Specifically, at Toronto Pearson International Airport (Pearson), wheelchair service is decentralized meaning that all airlines are responsible for organizing their own processes and service. This research uncovers some of the most commonly reported problems from both customers and employees regarding current wheelchair service at airports. This project finds that there is currently a fundamental communication gap between the employees and customers. Many passengers reported poor service due to not being about to navigate themselves through the process. Additionally, passengers report that the overall consignment of wheelchairs is often so poor that they are left waiting long periods of time for a chair and can sometimes be taken out of their chair before they are ready to walk. This project designed a mobile application interface that could provide both customers and employees with a way of communicating. This mobile application focuses on assisting employees with the organization of chairs and helps customers guide themselves through a more efficient process. This project applies itself to the model and processes that currently exist at Toronto International Pearson Airport.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.014 | 0.009 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.027 | 0.003 |
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".