Providing Accessible ReCreation Outdoors-User-driven Research on Standards: Novel method for winter assessments (Preprint)
Bibliographic record
Abstract
BACKGROUND Although there have been recent efforts to improve access to Canadian national parks, many remain not fully accessible to people with disabilities. Winter conditions, in particular, present challenges that limit their participation in outdoor activities. OBJECTIVE To develop a novel method to assess park access during winter that will inform recommendations for national park standards to meet needs of all park visitors (regardless of ability) during winter conditions. METHODS A larger participatory mixed-methods research project exploring park access was adapted [1]. Specifically, the second phase of the study to conduct in-person winter mobile interviews (i.e., walking and wheeling interviews) with people who have a wide range of disabilities while visiting three parks in two provinces was modified. Changes were made to accommodate the extreme winter weather conditions in Quebec while using safe and informative data collection methods. In Quebec, one park, where winter conditions are safer, will be assessed in person (n=4). Virtual interviews will be used to facilitate the assessment of other winter and summer conditions in two other parks (n=8). RESULTS - CONCLUSIONS We expect that adapting the protocol to gather further information on winter conditions and access to parks will provide high-quality and rich data to better inform park access standards. This participatory mixed-methods research will inform the development of park standards that consider the accessibility needs of all people.
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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 | 0.033 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.024 | 0.005 |
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".