Facilitating Inclusive Running Events: Policy Analysis to Reduce Barriers for Persons With Disabilities
Bibliographic record
Abstract
Through an Inclusive Design lens, this secondary research investigates the current state of accessibility for persons with disabilities (PWDs) within Toronto road-running events. The current academic literature demonstrates that PWDs benefit from participating in athletic and sports activities but that there are barriers preventing PWDs from participating. This research looks at the role event facilitators can play in reducing barriers for PWDs. The research uses evidence, in the form of policy documents, collected from event facilitators’ online public accessibility policies and, wherever possible, internal accessibility policies were also collected. The collected policies were then compared to the Accessibility for Ontarians with Disabilities Act, 2005 (AODA). The AODA is a Government of Ontario law that aims to ensure persons with disabilities (PWDs) have the same opportunity as people without disabilities in all aspects of daily life. Using Critical Discourse Analysis (CDA), the collected event facilitator’s accessibility policies were compared to the AODA. The research found that the current state of event facilitators’ accessibility policies varies widely and often does not comply with the AODA standards. As such, event facilitators must do more to comply with the AODA and work towards creating more inclusive road-running events.
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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 | 0.022 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".