An exploration of the lived acculturation experiences of newcomer varsity athletes in Manitoba
Bibliographic record
Abstract
Immigration has grown exponentially in Canada and, according to Sport for Life (2018), it will be the primary source of net population growth by 2030. Therefore, understanding the acculturation (learning a second culture [Rudmin, 2009]) challenges faced by the Canadian newcomer population has become essential. A gap related to understanding Canadian newcomer varsity athlete acculturation exists. This study explored the lived acculturation experiences of this population in Manitoba to reduce this gap and answer the call for more qualitative research related to this phenomenon (Schinke & McGannon, 2014). Using a social constructionist approach, letters to their younger self (inspired by Creative Analytic Practice) were used to inform semi-structured interviews from which data related to the participants’ acculturation experiences was collected. An interpretive thematic analysis (Braun and Clarke, 2006) was conducted in order to answer the question: What are the acculturation experiences of newcomer varsity athletes in Manitoba? The results revealed themes related to: a) the challenges athletes face settling in their host cultural context, b) the essential role of support systems to these athletes, and c) how context affects the athletes’ acculturation positionality. Particular insight into the social and structural aspects of the host culture, the networks of support that exist and how they can be improved will be discussed. Further, the practicality with which the athletes viewed adjusting to their host cultural context emerged as key to how the athletes experienced and approached acculturation in their host cultural context.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.023 | 0.008 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".