A Longitudinal Examination of the Accuracy of Perceived Physical Competence in Middle Childhood
Bibliographic record
Abstract
Children who underestimate their physical abilities have lower motivation, higher anxiety, and lack of understanding as to why they may be succeeding or struggling in sports settings, which can result in withdrawal from physical activities. Theoretically, middle childhood is a time when perceptions of physical competence (PPC) become more accurate as children develop the cognitive capacity to interpret new sources of feedback and develop a realistic sense of their physical abilities. The purpose of this study was to investigate the extent to which accuracy of PPC changed from grade 2 to grade 4. Participants were 238 boys and girls ( M age = 7.8 yrs) from eight participating elementary schools in Victoria, British Columbia, Canada. The Test of Gross Motor Development–Second Edition was used to assess motor skills. PPC were assessed using the Pictorial Scale of Perceived Competence and Social Acceptance for Young Children (for grade 2) and the Self-Perception Profile for Children (for grades 3 and 4). Results revealed that participants who underestimated or overestimated their physical competence in grade 2 saw an improvement in accuracy, and, by grade 4, had similar accuracy scores to their peers who were considered ‘accurate’ estimators. These results reinforce theory that suggests PPC become more accurate in middle childhood.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".