Innovating Youth Tournament Schedules to Minimize School Absenteeism
Bibliographic record
Abstract
Participation in sport has been lauded for the myriad benefits provided to youth who engage. Similarly, attendance in school has been identified as a salient contributor to academic success. Thus, the purpose of the present study was to explore the extent to which participation in youth representative (“rep”) hockey in Ontario contributes to avoidable absences from traditional school contexts. Specifically, empirical data from 104 youth rep hockey tournaments, ranging from AE-AAA competitive levels, and the Tyke (7-year-olds) to Midget (17-year-olds) age ranks, were utilized to meet the study’s first purpose. The second purpose was to present an alternative and innovative way youth sport tournaments could be scheduled to minimize school absenteeism. The results of the current investigation show there is merit to the proposed shift in tournament scheduling. Specifically, more than 42,000 avoidable school absences, from the 104 tournaments sampled, could be mitigated with a simple adjustment to tournament schedules.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".