Calgary Adapted Hub Powered by Jumpstart
Bibliographic record
Abstract
The Calgary Adapted Hub Powered by Jumpstart (https://www.calgaryadaptedhub.com/) aspires to be the go-to resource for inclusive and accessible sport and recreation programming in the city of Calgary. With the support of several partners, the Hub connects individuals and families of those with impairments to opportunities for adapted sport and physical activity to gain confidence, build friendships, and get physically active in new and exciting ways. Programs supported by Calgary Adapted Hub are then backed by industry leaders and evidence-based research, with quality coaching and instruction at every level. For the past four years the Hub has conducted research on the three pillars of health and wellbeing, social inclusion and economic impact / quality of life. The Hub has also hosted online knowledge translation seminars named in honour of Eli Wolff. The Hub has recently undergone an in-depth strategic review and is now embarking on its next step of evolution with a focus on social impact. In this presentation, Dr. David Legg, the Hub’s founder will review the history of the Hub and how this new focus will have practical implications. This presentation will thus address creating new opportunities to thrive and more specifically on Health and Well-being / Health, Article 25, physical activity and Sustainable Cities and Communities, Article 30.5, sport and leisure, participation in organized sport and physical activity.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.580 | 0.149 |
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".