Dil gelişimini değerlendirmede Edmonton Narrative Norms Instrument öykuleme aracının Turkçeye uyarlanması
Bibliographic record
Abstract
Adaptation of the Edmonton Narrative Norms Instrument narrative tool into Turkish for evaluating language development Aim: A narrative is one of the most common verbal language expressions that the children use very often to express themselves. The analysis of the narrative provides clinicians with detail information about how children’s expressive language development. The aim of our project is to adapt The Edmonton Narrative Norms Instrument (ENNI) narrative instrument into Turkish language to evaluate expressive language development of children. Materials and Methods: We assessed 356 typically developing children with the ENNI story set A, 261 typically developing children with ENNI story set B, and 87 hearing-impaired children with ENNI story set A and Turkish Early Language Development Test (TEDIL). All the assessed children were aged 4;0-8;0 years. The data including language samples was trancripted by language sample analysis program. IBM SPSS 2024 Software was used to conduct statistical analysis. Discussion and Conclusion: As a result of the study, the ENNI Narrative Instrument was adapted into Turkish, and a database was created for typically developing children in the A and B story sets and for children with hearing loss in the A story set. Keywords: Narrative, language development, analysis of language transcripts, ENNI
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".