Preferential Cup Size as a Predictor of End State Comfort in Children
Bibliographic record
Abstract
The end-state comfort (ESC) effect is an indicator of second-order planning related to object-manipulation, and is influenced by a number of factors, including hand preference and the properties of the object (e.g., size, orientation). The current research represents a preliminary data set which seeks to explore the occurrence of ESC planning in children when there is choice in preferred cup size. Children (N = 16; ages 9-10) performed a unimanual overturned cup task, first with a standard cup size (7.2 cm diameter), and then with a cup the size of their choosing (4.7-8.4cm diameter). Poisson regression were run for both standard cup size and student choice to assess predictors of ESC across the two tasks. Despite hypotheses and previous research, no predictors of ESC were found: hand size (standard: CI: .824-1.732, p = .349; choice: CI: .862-1.802, p = .243), and choice of cup (in the second condition; model effects p =.896) were not found to be significant predictors of ESC. Further exploration is needed to determine if change in hand size as a result of growth has an effect on predicting ESC in children Keywords: End-State Comfort; Second-Order Planning; Motor Planning; Grasp Selection Funding: NSERC Discovery Grant
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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 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".