An Investigation into Creating Accessibility on Campus for Disabled and Seniors
Bibliographic record
Abstract
The University of British Columbia is a major hub for pedestrian travel with thousands of people arriving to and from the campus on a daily basis. Many of these individuals who park on the university grounds are people with disabilities or the elderly, who struggle to travel from one location to another. There is no vehicle access or bus routes to many of these locations. With a campus area of 400 hectares (UBC Vancouver Campus, 2016), UBC needs a program that can assist these individuals with travel once they have already reached the university. The goal was to develop a solution using a small budget (relative to a new parking lot), to target seniors and the disabled, and to ensure ease of use. Using existing programs as a reference and estimating costs based on the products and services needed, the idea of a shuttle service was developed. The service is planned to use mainly golf carts for cost and navigation purposes as well as a few hybrid vehicles to ensure service during all weather conditions. The goal is to have volunteers to drive the golf carts and cars in order to minimize cost. To ensure ease of use, users will be able to call for a pickup or use automated buttons located across campus if they are not technologically proficient. This plan will allow for accessibility around campus, while maintaining budget goals and promoting sustainable solutions. Disclaimer: “UBC SEEDS provides students with the opportunity to share the findings of their studies, as well as their opinions, conclusions and recommendations with the UBC community. The reader should bear in mind that this is a student project/report and is not an official document of UBC. Furthermore readers should bear in mind that these reports may not reflect the current status of activities at UBC. We urge you to contact the research persons mentioned in a report or the SEEDS Coordinator about the current status of the subject matter of a project/report.”
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".