Navigating the "Brawn Drain": Canadian Student-Athlete Experiences in the U.S. and Canada
Bibliographic record
Abstract
There is an abundance of research on athletic scholarships and the experiences of college athletes (Duderstadt, 2000; Herbert, 2004; Miller & Kerr, 2002; Paskey, 2000; Sack & Staurowsky, 1998; Schofield, 2000; Shulman & Bowen, 2001). However, since much of this work has focused on the U.S. context, there is a scarcity of literature pertaining to the experiences of Canadian student-athletes. This study explores what is known as the "brawn drain"—the apparent movement of Canadian student-athletes to the U.S.--and compares their experiences with those who remain in Canada. In- depth, open-ended interviews with Canadian student-athletes at U.S. universities revealed that on the one hand, these athletes endured arduous training regimes, an increased pressure to perform athletically, and a higher value placed on athletic performance that at times compromised their academic priorities. On the other hand, interviewees noted their satisfaction with superior training facilities and the opportunity to continue to compete at a high level, benefits that they felt were not available in Canada. Our analysis is contextualized within the recent debates among and beyond Canadian Interuniversity Sport on the possibility of raising the annual cap on athletic scholarships in Canada (Paskey, 2000).
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.054 | 0.017 |
| Scholarly communication | 0.011 | 0.003 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".