Comparison of Academic and Behavioral Performance between Athletes and Non-athletes
Bibliographic record
Abstract
International Journal of Exercise Science 7(1) : 3-13, 2014. The Toronto Charter for Physical Activity (2010) and several national physical activity plans advocate sports participation as an important part of population targeted physical activity for youth. Emerging research evidence also suggests that sports participation during adolescents is linked to significant positive correlations with academic and behavioral performance. The purpose of this study was to compare academic and behavioral performance between male and female public school athletes (Total N=11,139; 38% Female) and non-athletes (Total N=23,891; 52% Female) in a convenient, ethnicity diverse, sample (grades 7 -12) from the state of Texas (USA). We examined the passing rates of individual athletes and non-athletes on standardized tests (Texas Assessment of Knowledge and Skills, TAKS) for math, language arts, reading, writing, science, and social studies. We also examined the percentage of athletes and non-athletes for being “at risk,” for dropping out of school and for the total average number of disciplinary actions. Chi-Square statistical analyses comparing athletes to non-athletes showed that athletes scored significantly better (ppp
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".