Sliding benchmarks might prevent de-selection of talented badminton players
Bibliographic record
Abstract
Despite potential advantages of talent identification practices, the degree of bias in decision-making due to relative age and maturity timing remains a concern. To investigate the impact of relative age and maturity on selection processes, and to examine the possible influence of an intervention aimed at minimizing the impact of relative age and maturity biases, thirty-three boys (Mage = 12.43y ± 0.36y) invited to compete for Badminton Malaysia, completed three anthropometrical measures, eight physical performance assessments, and five motor coordination tests. These players were tracked throughout their career to determine pathway progression (i.e., dropout or continuation) and their level of success (i.e., season-end rankings). With regards to the relative age of athletes and the initial selection to the U13 team, findings revealed that younger and less mature players were disadvantaged, since their morphology, physical fitness, and motor capacities were less developed than their peers. A sliding benchmark intervention was applied, where raw scores were adjusted. Although, the dropout rate from the U13 team was high (24/33 players, 73%), 6 of 9 remaining players of the national team achieved exceptional results, which were evident six years later. As a result of the sliding benchmark intervention, two relatively younger, late maturers with superior motor competence scores, were selected to the elite sport school. Without this intervention, both players might never have won the U21 World Championships. This paper examines the pathway of these competitive badminton athletes and discusses the potential value of applying a sliding benchmark intervention in competitive sport selection settings.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".