Canadian student-athletes on the move : narratives of transition through time and space
Bibliographic record
Abstract
Every year, approximately two thousand Canadian student-athletes, aged 17-23, cross the border to pursue their university education in the United States (Barnes, 2008). However, there is a scarcity of in-depth research that considers the experiences of these athletes. More pertinently, little is known about how student-athlete movement can be understood in relation to research on ‘detraditionalization’ and ‘individualization’ which suggests that the influence of traditional ties (e.g. class, gender, ethnicity) on young people’s experiences and identities is diminishing. With these concerns in mind a study was designed that explored the following two research questions: 1) how might processes of detraditionalization and individualization inform an understanding of the transition experiences of female Canadian student-athletes moving to the United States for their university education?; 2) how are female Canadian student-athletes’ experiences and identities shaped by the transitions they are faced with in their move to and time spent in the United States? To pursue these questions, in-depth interviews were conducted with 12 Canadian female student-athletes who pursued their education at and graduated from a United States’ university. Key findings included: the women’s decisions to attend college were influenced by an interrelated set of sport-related factors (e.g. a desire to pursue sport at the ‘highest level’) and non-sport factors (e.g. to break free from the familiarity of home); transition experiences were complex and context dependent and were also influenced by sport-related factors (e.g. relationships with the coach and teammates) and non-sport factors (e.g. degree of culture shock). The research also showed that the women used diverse strategies to deal with their disconnection from home (e.g. use of various communication technologies) and that negative experiences were commonly reframed in positive terms. In sum, the women experienced processes of individualization and detraditionalization but the structure of the sports team provided some of the stabilizing influences thought to be diminishing in the contemporary moment. This thesis concludes with a discussion of ways that these findings could be useful for athletes who are experiencing various forms of transition, and the coaches, peers, parents, and others who are attempting to support them.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.032 | 0.012 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".