Hand preference, performance abilities, and hand selection in children
Bibliographic record
Abstract
It is widely know that the pattern of human handedness is such that approximately 90% of the population is right handed with the remainder being left handed, at least in the adult population. What is less well understood is how handedness develops and at what age adult-like handedness patterns emerge. Quantified in terms of both preference and performance, a plethora of different behavioral assessments are currently in use with both children and adults. Handedness questionnaires are commonly used; however, these possess inherent limitations, considering their subjective nature. Hand performance measures have also been implemented; however, such tasks appear to measure different components of handedness. In addition to these traditional measures, handedness has been successfully assessed through observation of hand selection in reaching, which has proven to be a unique and effective manner in understanding the development of handedness in children. Research over the past several decades has demonstrated that young children display weak, inconsistent hand preference tendencies and are slower with both hands. Performance differences between the hands are larger for young children, and consistency improves with age. However, there remains some controversy surrounding the age at which hand preference and hand performance abilities can be considered fully developed. The following paper will provide a review of the literature pertaining to hand preference, performance abilities and hand selection in children in an attempt to ascertain the age at which adult-like patterns of hand preference and performance emerge.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".