An Innovative Approach to Increasing Youth Sport Participation: The Case of Baseball5™
Bibliographic record
Abstract
Youth sport participation preferences are evolving and shifting toward unorganized, nontraditional types of sport participation. This trend has left more traditional sports with decreasing participation numbers. Baseball Canada noticed a similar trend and therefore implemented an innovative approach to increase interest and participation in baseball. This case study follows Alex, the Manager of Sport Development at Baseball Canada, as they develop and evaluate Baseball5™, an innovative street version of the traditional sport of baseball. This alternative form of baseball needs to be tested and evaluated in five pilot programs throughout Canada. Alex collects survey, interview, and focus group data following each of the pilot programs to determine whether the approach is viable for increasing interest in baseball long term. After reading the case, students are tasked with analyzing the collected data and designing the Baseball5™ program for long-term implementation. The case is ideal for upper year undergraduate students who have the skills and knowledge necessary to execute program evaluations and build holistic program implementation plans, and for undergraduate courses in research methods or data analysis.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.014 | 0.008 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".