Catch the Feeling of Flying: Guided Walks Through a Trampoline Olympic Development Environment
Bibliographic record
Abstract
Olympic-medal performances represent peak accomplishments in athlete development. Seanor, Schinke, Stambulova, Ross, and Kpazai identified environmental factors in a high-performance Canadian trampoline sport environment that developed decorated Olympic medalists. The current intrinsic case study was authored to further highlight the idiosyncrasies of a high-performance trampoline environment (re)presenting stories garnered from this localized Canadian sport environment. Through guided walks, a mobile method of conversational interviews, three contextual experts who are engaged in the development of Olympic athletes provided tours of their sport environment. Each contextual expert’s guided walk played out uniquely in relation to his or her ascribed role (i.e., Olympic coach, assistant coach, and Olympic champion). Three main themes were identified through interpretive thematic analysis: creating lift (subthemes: facility design, sport-culture paragons), providing a tailwind (subthemes: establishing athlete–coach partnerships, team interactions), and soaring onto the Olympic podium (subthemes: preparing athletes to be untethered, competitive collaboration). Each theme is presented through three portrait vignettes, with discrete vantages derived from each contextual expert to illuminate the context from idiosyncratic ascribed roles within the environment. These stories create a rich (re)presentation of a high-performance sport environment through the interplay of the contextual experts’ narratives, their surrounding context, and their Olympic-podium accomplishments.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.020 | 0.017 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| 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".