Building Adapted Physical Activity Collectives in Canada: Challenges and Opportunities
Bibliographic record
Abstract
Meaningful collaboration and partnerships are crucial for successful adapted physical activity communities, but they are hard to create, manage and sustain. In this presentation three speakers will reflect on the various local (e.g., Calgary Adapted Hub Powered by Jumpstart https://www.calgaryadaptedhub.com/ and OneAbility https://www.oneability.ca/) , provincial (Alberta Inclusive Sport and Recreation Collective and The Steadward Centre (https://www.ualberta.ca/en/steadward-centre/index.html), and national (National Adapted Sport Collective and Canadian Disability Participation Project https://cdpp.ca/), collectives they have created, led and participated on related to adapted physical activity. With a focus on the conference themes of ‘Inclusivizing’ our world and ‘Nothing about us, without us’, the panelists will speak to the impact of collective work on building more inclusive communities, while acknowledging the challenges that come with ensuring adequate engagement and representation of those with lived disability experience. Attendees will be encouraged to reflect on their own communities and how they can use collaboration and partnership to have a positive impact on adapted physical activity participation.
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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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.050 | 0.008 |
| Scholarly communication | 0.012 | 0.004 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.011 | 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".