Performance dispersion for evidence-based classification of stationary throwers
Bibliographic record
Abstract
BACKGROUND: There is a need for better understanding of the dispersion of classification-related variable to develop an evidence-based classification of athletes with a disability participating in stationary throwing events. OBJECTIVES: The purposes of this study were as follows: (1) to describe tools designed to comprehend and represent the dispersion of the performance between successive classes and (2) to present this dispersion for the elite male and female stationary shot-putters who participated in Beijing 2008 Paralympic Games. STUDY DESIGN: Retrospective study. METHODS: This study analysed a total of 479 attempts performed by 114 male and female stationary shot-putters in three F30s (F32-F34) and seven F50s (F52-F58) classes during the course of eight events during Beijing 2008 Paralympic Games. RESULTS: The average differences of best performance were 1.46 ± 0.46 m for males between F54 and F58 classes as well as 1.06 ± 1.18 m for females between F55 and F58 classes. The results demonstrated a linear relationship between best performance and classification while revealing two male gold medallists in F33 and F52 classes as outliers. CONCLUSIONS: This study confirms the benefits of the comparative matrices, performance continuum and dispersion plots to comprehend classification-related variables. The study presented here represents a stepping stone into biomechanical analyses of stationary throwers, particularly on the eve of the London 2012 Paralympic Games where new evidences could be gathered.
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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 | 0.041 | 0.145 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.022 | 0.010 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".