Examining Olympic coach's journey through video ethnography
Bibliographic record
Abstract
This paper discusses the results of a research project which aimed to capture, explore and communicate the occupation and complex development of an Olympic coach. The researcher used an innovative research process utilizing Video-Ethnography as a tool to better understand the coaching culture with a unique representation of its intricacies (Sparkes, 2002). The video ethnography study was carried out over a one year period during training sessions and three main international competitions; Commonwealth Games, World Championships and Olympic Games. The case study illustrated the career journey of a five-times Olympic coach of middle distance athletics which involved much more than the predictable roles and responsibilities. The study focused on reviewing, with the coach selected video footage from his practice sessions and competition, with reflexive viewing and constructivism of narrative reality. The coach developed a theme system drawing from the reflexive viewing of the footage and assembled a story board for narration. The aim of this research paper was to better understand the complexity involved in coaching and the journey of the coaching expertise development. As a critical agent in mediating the development of athletic proficiency this reflexive viewing evoked a better understanding of the coach's expertise development.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".