Understanding the Adaptation Strategies of Canadian Olympic Athletes Using Archival Data
Bibliographic record
Abstract
Athletes employ a variety of adaptation strategies when adjusting to competitive environments. Fiske (2004) identified five core motives that facilitate human adaptation: (a) understanding, (b) controlling, (c) self-enhancement, (d) belonging, and (e) trusting. Recent qualitative analyses (Schinke, Gauthier, Dubuc, & Crowder, 2007) revealed that these motives correspond to particular adaptation strategies that professional athletes employ in stressful settings. The present study uses analysis of archival data (i.e., journalistic accounts) to explore the adaptation efforts of Canadian Olympic athletes (N = 103) as they prepared for and participated in summer (n = 35) and winter (n = 68) games. Contextual experts with extensive Olympic experience were enlisted to clarify the archival record. Findings revealed that the Olympic athletes used strategies corresponding to each of Fiske’s five motives, as well as numerous specific substrategies. Use of substrategies was consistent across athletes, regardless of Olympic experience, gender, or season (e.g., winter or summer games). Discussion explores the implications of adaptation strategies for Olympic athletes and their supporting staff.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| 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".