Motives for and Experiences of Expatriation to Coach
Bibliographic record
Abstract
Coaching is a global profession and coaches play a central role in enhancing the performance of millions of athletes worldwide. In the 21st century, the global mobility of coaches has increased, with many coaches taking advantage of opportunities to coach abroad. Norway leads the all-time Winter Olympics medals table (i.e., 368 medals), and with most of these medals coming from skiing disciplines, Norway represents a skiing hotbed that attracts ski coaches from other parts of the world. The purpose of the study was to examine ski coaches’ motives for and experiences of expatriation to coach in Norway. Five North American alpine ski coaches (four males and one female) were individually interviewed (M = 77 min, SD = 24.94), with the data examined using interpretative phenomenological analysis. Motives for expatriation included having a passion for skiing, challenging oneself, experiencing a new sport culture, and maintaining relationships. Upon arriving in Norway, coaches mentioned experiencing challenges with the Norwegian sport system, language, pressure from parents and the media, and being far from friends and family. Once acclimated, coaches discussed the benefits of expatriation that included the Norwegian work ethic, family-centric lifestyle, and popularity of skiing.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.006 |
| 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".