A user-led audit of the walkability and wheelability of Quebec City’s neighborhoods by mobility assistive technology users
Bibliographic record
Abstract
For mobility assistive technology (MAT) users, environmental obstacles or helpful elements can make the difference between disabling situations and social participation. User led environmental evaluations can highlight difficulties MAT users experience, which can inform changes to the built and social environment. This study used the Stakeholders’ Walkability/Wheelability Audit in Neighbourhood People with Disabilities (SWAN-PDW) to identify observable (objective) and experienced (subjective) barriers/obstacles and facilitators/helpful features encountered by 25 MAT users in their daily lives in three residential environments (i.e. urban, semi-urban and suburban) in Quebec City (Canada). Because the participants’ were directly involved in the identification of obstacles and helpful elements, this type of user-led evaluation may empower MAT users to initiate discussions with the relevant authorities. By acknowledging the difficulties and opportunities encountered by MAT users, stakeholders can use these individuals’ expertise in the planning and decision-making processes to improve access for all citizens.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.013 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".