Laterality in sport: Does sport-specific training impact everyday limb preference?
Bibliographic record
Abstract
Factors such as preference, context, and location of an object in space affect whether an individual will reach across the midline of the body in a specific movement (Bryden, Scharoun, & Dargavel, 2014). The high demand for bilateral limb-use in sport-specific training may also influence limb selection. Whether limb choices within sport are specific to training or not, discrepancies in everyday actions may develop relative to what choices would be made without such training. To test this prediction, 10 athletes (volleyball, baseball, soccer) and 10 non-athletes completed questionnaires (Waterloo Handedness Questionnaire, athletic profile) and a preferential reaching task that consisted of manipulating wooden dowels and rubber mallets located at 3 equidistant locations (left, right, midline) in 2 conditions (pick-up, use). Limb choice for manipulation was recorded as the dependent measure. Findings provided preliminary evidence supporting the hypothesis that athletes would demonstrate more bilateral preference than their non-athlete counterparts. As differences were found predominantly in left space, sport-specific training may play a role in athletes displaying a higher degree of comfort and competence with their non-preferred limb. A tentative explanation for this might be that comfort and competence levels with a non-dominant limb are impacted by years of high-level sport-specific training in sports requiring bilateral limb use. Data collection will continue with the goal of disentangling this impact based on the type of sport played.Acknowledgments: This research was funded by a start-up grant from the University of Windsor
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.004 | 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".