Narrative to Investigate Language Skills of Preschool Children
Bibliographic record
Abstract
This study aimed to evaluate the language skills of preschool children through narrative.The Turkish Test of Early Language Development (TEDIL) was used to evaluate the receptive and expressive verbal language skills of the children, and language samples were collected using the Edmonton Narrative Norms Instrument (ENNI).The Mean Length of Utterance (MLU), Number of Different Words (NDW), and Total Number of Words (TNW) were examined in the language samples taken from the narrative analysis.A total of 100 children, 50 in the age group of 48-60 months and 50 in the age group of 61-72 months, were evaluated.According to the results obtained from the evaluation of the language skills of the children between the ages of 48 and 60 and 61 and 72 months, it was found that, children between the ages of 61-72 months were more likely to tell longer stories than the children of 48-60 months.It was seen that there were developmental differences in NDW and TNW in the stories of children between these two age groups.It has been revealed that the ENNI can be used as a language tool to assess the language skills in preschool children.Narrative skills show that a child can talk about his/her life beyond the use of grammar.Early narrative skills in children require a high level of language and cognitive skills.Stories are far more than the flow of unrelated words and sentences.Storytelling requires the use of complex and consistent linguistic structures.In order to create a coherent
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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 | 0.003 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".