Linking the preference in a bilateral asymmetric task with handedness, footedness, and eyedness: The case of ice-hockey
Bibliographic record
Abstract
Most people know whether they are left-handed or right-handed, and usually base this assessment on preferences during one-handed tasks. There are several manual tasks that require the contribution of both hands, in which, in most cases, each hand plays a different role. In this specific case, holding an ice-hockey stick is particularly interesting because the hand placement may have an incidence on the playing style. In this study (n = 854), the main objective was to determine to what extent the way of holding an ice-hockey stick is associated with other lateralized preferences. Amongst the 131 participants reporting a preference for the left hand in unilateral tasks, 70.2% reported a preference for shooting right (placing the right hand in the middle of the stick); and amongst the 583 participants reporting a preference for writing with the right hand, 66.2% reported a preference for shooting left. 140 (16.4%) participants were classified as ambidextrous and 61.4% of them reported a preference for shooting right. This preference on the ice-hockey stick is closely correlated (uncrossed preference) to the way one holds a rake, shovel, or broom, or a golf club, but inversely related to the way one holds an ax and a baseball bat. The link between the way of holding the ice-hockey stick and eyedness or footedness is weak. These results are contrasted with the results reported by Loffing et al. (2014) and reveal the need to clarify the exact nature and requirements of the targeted tasks when studying bilateral asymmetric preferences.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".