Peer Mentoring Canadian University Student-Athletes: An Analysis of NCAA Division I Academic Mentoring Program Handbooks
Bibliographic record
Abstract
For many Canadian students, varsity athletics is an important part of their University experience. Prior to attending University many high level athletes are greatly influenced by their parents and/or extended family, and once at University that role is often replaced by their teammates and peers. Some students are fortunate to find a positive mentor-like figure in a veteran player. However, too often this is not the case, and bad academic habits are developed early before the student-athlete has a chance at academic success. Transitioning into post-secondary education is challenging enough for students who are not on a varsity team, and student-athletes are expected to balance twice as much responsibility. A university’s reputation is affected if student-athletes are continually forced to withdraw from their studies, providing an even further disadvantage for athlete recruitment. It is the university who is allowing student-athletes to take on additional responsibility to represent the university and even accepting student-athlete who are not as academically prepared. Therefore, it should be the university’s responsibility to provide proper assistance and support, because student-athletes should not be sacrificing their academic experience to play their sport. All students, including student-athletes, should be graduating with the same education and skills. Anthony Giddens’ Structuration Theory looks at the recursive nature and “duality” of structure (Orlikowski & Yates, 2007). When applying the principles of structuration theory in a grounded theory analysis of five National Collegiate Athletic Association (NCAA) academic mentoring handbooks, it becomes clear that when student-athletes enter university they are entering a completely different social structure and university experience than non-athletes. What becomes clear is that any assistance and support needs to be tailored to student-athletes at that specific institution, and different from non-athletes. Furthermore, implementing an athlete academic peer mentoring program could help to change negative views of academics that have developed in the student-athlete social structure. Considering that Kerr and Miller (2002) found Canadian university student-athlete to be experiencing similar challenges to those in the NCAA, then they should also have provided to them academic assistance specific to their needs.
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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 | 0.006 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.009 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".