The Influence of Teacher Coaches on Canadian Secondary School Student Athletes
Bibliographic record
Abstract
The success of Student-Athletes (SA) both in-class and on the field depends largely on two particular individuals: the SA themselves and his or her Teacher Coach (TC). TCs within a given school have a duty to the classroom as the teacher, as well as a duty to the particular sports team(s) they coach. SAs are, just as the name would suggest, attending school primarily to be students and secondarily as athletes. The TC is in a unique position to create a bond with their SAs where they can nurture athletic prowess while also promote the importance of the students' academic performance. This study will explore the ways in which TCs in Canadian secondary schools motivate and influence their SAs to succeed both as students, as well as athletes. A large body research has been conducted specifically examining Canadian secondary school SAs who receive athletic scholarships to universities outside of Canada. There exists a noticeable gap, however, in the research aimed towards understanding the plight of the Canadian SAs who will not receive full scholarships to attend post-secondary institutions. This research project will offer a qualitative description of how TCs are aiding their SAs towards achieving success both in the classroom, as well as on the field of play, in order to graduate from high school and attend a post-secondary institution, should they be so inclined.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.017 | 0.004 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".