Preschool Children’s Speech Pedagogical Sound Culture Correction
Bibliographic record
Abstract
Objective: The article aims to reveal the features of correction of the sound culture of the preschool-age children's speech, the effectiveness of which has been tested experimentally. Background: The sound culture of speech is a multicomponent formation, which covers the phonetic correctness of speech; general language skills and orthoepic correctness of speech. Pedagogical correction of the sound culture of speech is focused on the correct the errors caused by a violation of the sound articulation, sound pronunciation, orthoepic norms of pronunciation, voice strength, etc. Method: In the study, the author's method of pedagogical correction of the sound culture of children’s speech was used. Also, it was used comparative analysis and method of successive analysis of adjustment variants of the speech sound culture. Results: An individual model of pedagogical correction of the sound culture of the child's speech was developed. Training to deepen knowledge, improvement of abilities, and skills of teachers were held. The exercises in sound pronunciation and intonational speech expressiveness were developed. Conclusion: Positive dynamics of developmental levels of the sound culture of children’s speech, which has been confirmed by the results of quantitative and qualitative analysis, confirms the effectiveness of the experimental methods of pedagogical correction of the sound culture of speech.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".