Intra-limb coordination in boys with and without Developmental Coordination Disorder (DCD)
Bibliographic record
Abstract
The purpose of this study was to determine differences in the nature and effectiveness of intra-limb coordination in boys with and without DCD, in one-handed catching. Ten boys, in each group, attempted to catch at ten balls at 7m/s. Two high-speed cameras (100Hz) were used to derive 3D coordinates. The degree of coordination was inferred from the magnitude of correlation coefficient between angular displacement of the relevant joints; higher values indicated tighter coupling. Standard deviation, calculated across five trials, was used to infer stability. Results showed a significant interaction effect ( F =10.64, p 2 = .39) for the degree of spatial coupling, and subsequent planned comparisons revealed significant differences between the groups for shoulder-elbow (Sh-El) ( w/o DCD = .69; DCD = .80) and elbow-wrist (El-Wr) ( w/o DCD = .79; DCD = .61) joint pairs. Also, a significant group main effect was found for stability ( F (1, 18) = 7.97, p 2 = .32) for the El-Wr ( w/o DCD = .11; DCD = .21). In terms of effectiveness, boys w/ DCD caught fewer balls ( w/o DCD = 85%; DCD = 32%). Contrary to previous work, no universal coordinative tendency emerged for either group confirming that (intra-limb) coordination is dependent on many different constraints. The potential causes of failures exhibited by DCD will be discussed in the context of relevant models of redundancy.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".