THE PROJECT “STAR-LITTLE STAR”. STRENGTHENING 4-AND 5-YEAR-OLD CHILDREN’S SOCIO-EMOTIONAL AND COLLABORATIVE SKILLS THROUGH SINGING AND REPRESENTING A SONG VISUALLY AND DRAMATICALLY
Bibliographic record
Abstract
The present study explores the attitudes of early childhood children towards activities that combine the arts on the occasion of an original song and its visual and dramatic representation. The project was carried out in the context of a nursery school/kindergarten children’s (N=20) participation in a song contest that was conducted by “Make a wish” of Greece, an organization that aims to support children who are struggling with serious illnesses. The aims of the study were to cultivate children’s collaborative and social-emotional skills, to entertain them and to provide opportunities to encourage their creativity. The project was carried out in 4 stages: a) creating an original song on the theme of a star that makes wishes come true, b) young children learning to sing the song, c) recording the song in the studio, d) visual representation of the song working individually and in groups, d) creating of a video presentation that included the young children's paintings with the song as background music, d) dramatization of the song and its performance at the summer celebration of the school. The data were collected through observation, diary notes and children's products. General findings show that the young children were given opportunities to cultivate their creativity through their engagement in the activities, have fun, cooperate with each other, feel part of a larger group and cultivate social-emotional skills such as empathy, self-confidence, learning to accept the criticism of others and respect others’ views. Article visualizations:
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
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".