Accessibility Analysis and Mapping at the University of British Columbia
Bibliographic record
Abstract
The purpose of this dataset is to allow for an accessibility analysis and mapping project to be conducted on the University of British Columbia (UBC), Vancouver Campus. In a university setting, campus navigation is a foundational part of seizing opportunities, networking with other scholars, and having an all-around positive student experience. Yet, inaccessible features of an urban landscape (like stairs, rough terrain, or steep slopes) often leave mobility-limited individuals at a great disadvantage or cut off from certain opportunities. The accessibility analysis and mapping project (AAMP) is geared to try and answer what and where barriers to wheelchair accessibility exist on the UBC campus. To do this, (1) two cost paths for accessible and inaccessible terrain were calculated and compared to identify barriers to accessibility, (2) a least-cost path analysis is conducted to test if wheelchair routes are statistically longer than walking routes, and (3) a wayfinding map geared toward wheelchair users is created with the intention of increasing campus navigation equity and as a visualization for urban planners to see where campus accessibility improvements need to be made. It was discovered that 10% of the total walkable path area was some sort of accessibility barrier to wheelchair users. Through a visual investigation and comparison with the previous literature, three main types of barriers were identified on the campus. Next, an online map was created of the study site which highlighted accessibility barriers and difficult terrain. Finally, the paper ends with a discussion around why certain types of accessibility barriers exist on the campus and what urban planners can do to fix these and create more equitable wayfinding experiences across urban landscapes.
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 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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.011 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.028 | 0.010 |
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".