The Relative Age Effect on Physical Fitness among 12 Years Old Children in Malaysia
Bibliographic record
Abstract
The purpose of this study was to identify differences in fitness levels between children who are the same age but were born in different birth quartiles. 98 year six students (12 years old) from all around the state of Perak, boys and girls who do not have any unusual health conditions were used for the samples. Based on their quarter of birth, all children were divided into quarters: quarter 1 (q1: born between January and March), quarter 2 (q2: born between April and June), quarter 3 (q3: born between July and September), and quarter 4 (q4: born between October and December). Fitness tests will include body composition measurements (body weight, standing, and sitting heights, and arm span), standing broad jump test, sit and reach test, 30-meter sprint test, handgrip, step test, curl up test, stork stand test, and T-Test. The distribution of the participants was 98 children aged 12 years old (n =31 (31.6%), n = 27 (27.6%), n = 21 (21.4%) and n = 19 (19.4%)), from quarter 1, quarter 2, quarter 3 and quarter 4, respectively. A multivariate analysis of variance (MANOVA) was used to compare four birth quartiles on twelve physical fitness tests. The multivariate test of the differences among the four groups was significant, f(12,85)=.043, Roy’s Lambda = 0.272. Children born in the first quarter of the year were found to be more talented than those born in the last quarter.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".