The Adaptation Process of National Hockey League Players
Bibliographic record
Abstract
Through adaptation studies in elite sport, researchers can delineate the strategies that amateur and professional athletes employ during career transitions (e.g., promotion, relocation). Fiske (2004) identified five core motives as catalysts to adaptation: understanding, controlling, self-enhancement, belonging, and trusting, which were recently contextualized in sport as a result of one archival study examining the second hand experiences of National Hockey League (NHL) players. The purpose of the present study was to learn about the adaptation process of NHL players based on a first hand data source (i.e., semi-structured interview). A semi-structured open-ended interview guide was utilized to learn about the experiences of four groups of NHL players (n = 11): prospects (n = 3), rookies (n = 3), veterans (n = 2), and retirees (n = 3). There is an indication that adaptation strategies and sub-strategies vary according to the player’s career stage and the challenges related to seeking and maintaining a roster spot. The findings are also consistent with Fiske’s five core motives and earlier adaptation sub-strategies, in addition to uncovering three novel sub-strategies (i.e., understanding one’s performance, distraction control, and trusting player agents). Implications and recommendations are provided for sport researchers and practitioners.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".