FEATURES Academic Comparison of Athletes And Non-Athletes in a Rural
Bibliographic record
Abstract
This study compares academic performance, behavior, and commitment of basketball and volleyball athletes and non-athletes in a rural Canadian high school. It compares mid-term and final grades in each school discipline; visits to an administrator for disciplinary visits; and demerit points for improper behavior; and estimates the mean weekly time commitment for athletes in each sport. The study provides sup-port for the benefits of athletic programs at the high school level. any believe that athletics in school is contrary to values that pro-/ / mote learning and academic performance. Carlson (1993) Mdescribes an urban high school noted for producing civic, educa-tional, and business leaders. As the school rose to prominence in athletics, it declined in academic performance. Instead of graduating and boasting of strong community and business leaders, the school now boasts of students who have been drafted and play in professional sports. Other studies found conflicting results. Haynes (1990) cites a study of 58,000 high school juniors and seniors that found athletes were general-ly better students except for the two &dquo;big&dquo; sports, football and basketball. Maloney and McCormick (1993) support the finding that athletes in revenue sports do not, on average, perform as well in the classroom as their non-athlete peers. Athletes in the non-revenue sports had no difference in grades with non-athletes. Hood, Craig, and Ferguson (1992) reported that
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".