Your fate is in your hands? Handedness, digit ratio (2D:4D), and selection to a national talent development system
Bibliographic record
Abstract
Over the past decade a small evidence base has highlighted the potential importance of seemingly innocuous variables related to one's hands, such as hand dominance and the relative length of the second and fourth digits (2D:4D ratio), to success in sport. This study compared 2D:4D digit ratio and handedness among handball players selected to advance in a national talent development system with those not selected. Participants included 480 youth handball players (240 females and 240 males) being considered as part of the talent selection programme for the German Youth National team. Hand dominance and digit ratio were compared to age-matched control data using standard t-tests. There was a greater proportion of left-handers compared to the normal population in males but not in females. There was also a lower digit ratio in both females and males. However, there were no differences between those selected for the next stage of talent development and those not selected on either handedness or digit ratio. These results add support for general effects for both digit ratio and handedness in elite handball; however, these factors seem inadequate to explain talent selection decisions at this level.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".