Bibliographic record
Abstract
The timing of children’s start to school is the interest of researchers, policy-makers and especially parents. Researches on this issue have recently pointed to the concept of relative age effect (RAE). The purpose of this study is to determine the impact of RAE on mathematics achievement in Turkey. In order to accomplish this purpose, the question was sought: Does RAE have an impact on TIMSS 2015 the fourth and eighth grades mathematics scores of Turkey? The research was conducted in survey model. In the present study, the data obtained from the TIMSS 2015 results of Turkey was used. Totally 6456 students were sampled for TIMSS 2015 the fourth grade in 242 schools. Also, totally 6079 students (2943 girls and 3136 boys) were sampled for TIMSS 2015 the eighth grade in 218 schools. Hierarchical multiple regression analysis was used to analyze the data. In conclusion, it has been reached that RAE has an impact on TIMSS 2015 the fourth and eighth grades mathematics scores of Turkey and the youngest children born just before the cut-off date has the worst performance. The recommendations based on the results have been submitted as making the enrollment dates more flexible, especially for children in rural areas, not applying honors classes in schools, raising awareness about RAE in pre-service and in-service training programs for teachers, no pressure on children at home and at school for their lagging in competition.
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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 | 0.010 |
| 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) | 0.008 | 0.001 |
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".