Sport et virtualisation, quelles pratiques golfiques pour quelle utilisation? Approche exploratoire du golf en France
Bibliographic record
Abstract
Le sport vit aujourd’hui un tournant majeur. Les pratiques se numérisent, les outils se développent et la virtualisation gagne du terrain. Applications connectées, partage et comparaison de données et autres évolutions mènent à l’apparition de technologies de réalité mixte. Nous nous interrogerons dans ce travail sur la pénétration de ces technologies dans le monde du golf et les usages qui en sont faits par les différentes typologies de golfeurs en France. Au travers d’entretiens individuels, nous remarquerons que le golfeur loisir semble se diriger vers une pratique ludique, alors que le compétiteur ou le professionnel de golf semblent privilégier la technologie d’accompagnement à la performance, d’aide à l’entrainement. Toutefois, les technologies existantes ne semblent pas encore suffisamment immersives pour remplacer le golf dans sa pratique traditionnelle.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".