Understanding the characteristics that contributed to Québec athletes’ olympic successes moving into, during, and out of the Olympic Games
Bibliographic record
Abstract
The current study examined the characteristics that influenced four of the most successful Québec athletes’ performances moving in, during, and out of the Olympic Games. Their experiences were explored using media data (i.e., newspapers articles from the province of Québec) through an inductive thematic analysis. Seven main practical conclusions were created from this study with the first five relating to the three meta-transitions explored, and the last two relating to limitations created by the the methodology used in the study. The conclusions are: 1) Developing an empowering coach-athlete relationship in order to facilitate the athletes’ performances, 2) Starting a competition easy to finish strong, 3) Creating realistic but challenging goals for every step of the Olympic season, 4) Consciously learning form success and failures/mistakes, 5) Using mainly intrinsic motivation when competing, 6) The unmentioned importance of sport psychology services, and 7) The omission of information about provincial versus national pride.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".