Bibliographic record
Abstract
Aggression observed in early childhood is considered to be an important problem. Multiple factors may play a decisive role in children’s aggressive behaviors. The aim of this study was to examine whether the receptive and expressive language skills of preschool children (39-75 months-old) had predictive role on the levels of physical and relational aggression. The sample of the present study consists of 109 preschool children (47 girls and 62 boys). The language development levels of children in the sample were evaluated by TEDIL (Turkish Early Language Development Test) and their aggression levels were evaluated via Preschool Social Behavior Scale which were filled by their teachers. Pearson product moment correlation coefficient and stepwise regression analysis were used to analyze the data. According to the findings of the study, there was a significant and negative relationship between children’s receptive and expressive language skills and physical and relational aggression levels. The results of regression analysis revealed that physical aggression was negatively and significantly predicted by expressive language skills. In addition, relational aggression is negatively and significantly predicted by both receptive and expressive language skills. The increase in children's language skills significantly explains the decrease in physical and relational aggressive behaviors. The relationship between language skills and aggressive behaviors of Turkish preschool children was examined for the first time in this study. It is thought that the present study will contribute to the literature since it reveals the current situation in terms of the relationships between children's language skills and aggression levels and provide opportunities to make comparisons with the results of international studies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".