WINTER: PUBLIC ENEMY #1 FOR ACCESSIBILITY EXPLORING NEW SOLUTIONS
Bibliographic record
Abstract
Abstract: Winter is expensive. For countries situated in the northern hemisphere, closer to the north pole, such as Canada, Russia and Scandinavia, winter requires the acquisition of special clothing, car tires, and sports equipment, snow removal or plowing from the streets, and is associated with the presence of ice patches, along with accidents and illnesses associated with cold weather. Fall-related injuries due to winter conditions have been estimated to cost the Canadian health care system $ 2.8 billion a year. However, the greatest cost snow entails every year is the social isolation of seniors as well as wheelchair and walker users. This results from the lack of accessibility, as it is difficult to circulate on snow-covered streets even for the able-bodied. Social isolation has been associated with other negative consequences such as depression and even suicide. This exploratory pilot study aimed at finding possible and feasible design solutions for improving the accessibility of sidewalks during winter conditions. For this project we used a Co-Design methodology. Stakeholders (City of Quebec representatives, designers, urban planners, occupational therapists, and adults with motor, visual and aural disabilities) were invited to participate in the design process. In order to meet the objectives, two main steps were carried out: 1. Conception of the design solutions (through Co-design sessions in a Focus-group format with seniors, designers and researchers); and 2. Validation of the design solutions (consultation with experts and stakeholders). The results are a wide variety of possible and feasible solutions, including the reorganisation of the snow-removal procedure and the development of heated curb cuts. This project was funded by the City of Quebec in partnership with the Centre interdisciplinaire de recherche en réadaptation et intégration sociale (CIRRIS). Ultimately, the project sought to explore possible solutions to be implemented, if feasible, in the future by the municipal government
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".