Children’s Self-Perceived and Actual Motor Competence in Relation to Their Peers
Bibliographic record
Abstract
Motor skill competence enables children to move with efficiency and confidence in a variety of physically challenging situations. A child who lacks motor skill competence may be less inclined to take part in physical activities in which his or her peers excel. In this regard, the development of motor competence and children’s perception of their motor abilities may play an important role in ensuring sufficiently physically active adults. To better understand the role of motor competence in children’s participation in physical activity, this study examined children’s perception of their motor competence in comparison to others with their actual motor competence. Data were collected from 1031 children in grades 3, 4, 5, and 6 and between the ages of 8 to 12 years from elementary and junior schools. Using the Canadian Assessment of Physical Literacy (CAPL) protocols, physical competence and perceived physical competence were obtained from the Canadian Agility and Movement Skill Assessment and the CAPL questionnaire, respectively. Results from this study support previous research as children’s ability to accurately perceive their motor competence increased with age/grade. Still, over half of the participants in this study were not able to accurately perceive their motor competence. In addition, as grade increased from 3 to 6, children over-estimated their abilities less and underestimated their abilities more. This lack of ability to accurately estimate their abilities may be impacting children’s level of physical activity and should be addressed when promoting physical activity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| 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".