Examination of the Relationship between Demographic Characteristics of the Family and the Language Development of Children
Bibliographic record
Abstract
The aim of this study is to determine the relationship between the demographic characteristics and the language development of children. In the research, a "Personal Information Form" consisting of 14 items containing information about the demographic structure of the family was used and a "Language Development Checklist" consisting of 25 items that the students are required to possess the language skills in the learning process was used. The sample of the study consists of 147 children who are studying in Ağrı province center determined by purposeful sampling method. Descriptive statistics, t-test and one-way analysis of variance (ANOVA) and Tukey test of multiple comparison tests were used for the analysis of data in the study. As a result of the research, it was determined that there is a direct relationship between the demographic characteristics of the family and the language development of the children. The increase in the level of income and the level of educational background of the parents has influenced the language development of the child; besides, families with democratic attitude have been found to be more successful in terms of children's language development.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".