Mitigating the Physical Barriers of a Post-Secondary Education: Accessible Mobility Mapping and Rollshed Analysis for Vancouver Island University
Bibliographic record
Abstract
This project provided the Vancouver Island University (VIU) Nanaimo campus with a detailed accessible mobility (AM) map and rollshed and routing analysis. The VIU campus consists of numerous steep pedestrian pathways that complicate the navigation of mobility-limited individuals. The goal was to mitigate physical barriers in the built environment by providing campus pedestrians with wayfinding and navigational information while simultaneously supporting the future AM work by VIU administration and facilities. Additionally, it was desired that the AM mapping methodology of this project be reproduceable for other institutions. First, a data typology was developed, and data was collected for several aids and barriers to AM. The data was refined into a campus map by categorizing pathway slope into accessible, steep and very sleep classes, and adding additional AM and ancillary information. The data was again refined to produce the rollsheds and AM routes. An average travel speed, path costs and path barriers were identified and used in a service area analysis to determine the distance a manual and powered wheelchair user could travel in a set amount of time. The map was released in September of 2019 and has reduced the amount of AM wayfinding and navigation questions received by Disability Access Services. The general methodology of the map is reproduceable, however it requires that an analyst make decisions that will ensure it depicts the most crucial barriers and aids to mobility found in the built environment. The rollsheds and routes highlighted AM weaknesses in the pedestrian network and is important information for the VIU administration and facilities to consider when discussing future plans for the campus.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".