'Sent down? Called up?': Exploring the roller coaster of loans and re-assignments in professional hockey
Bibliographic record
Abstract
Athletes constantly face transitions in their sporting careers, which can influence the quality of their performance and well-being. The purpose of the study is to explore professional hockey players’ lived experiences with being called up and sent down in organizations. For example, an athlete can play in the National Hockey League (NHL) and is then sent down to their affiliated team in the American Hockey League (AHL) for a variety of reasons. The study utilized a phenomenological approach to understand athletes lived experiences with being called up and sent down, this allowed the researcher to move beyond brief descriptions toward understanding this specific transition athletes face. Semi-structured interviews were audio-recorded and transcribed verbatim, which occurred with six current hockey players (five current professional athletes and one competitive athlete). Data-analysis followed a two-phase process analysis to determine themes and patterns within each interview and then compared patterns across interviews to see what is common across interviews. The results were presented in three clusters such as the performance and well-being of an athlete, external influences on career, and interpretations of experiences. Further research is needed to explore the impact that loaning can have on an athlete and their well-being.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.010 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".