The expression of emotions and beliefs in the narratives of 5 to 10 year-old children: Effects of an intervention procedure
Bibliographic record
Abstract
The capacity to express emotions and beliefs in an articulate way is central to social communication. Children start early to express emotions in their everyday conversation with familiar partners and can refer to them to narrate personal experiences. However, talking about the internal world of the characters of a story is a later competence, undergoing development through 10-12 years. Since narrative abilities are predictive of literacy and academic achievements, finding a way to help children develop their skills in this domain appears an important goal. This paper studies the effects of a conversational interaction procedure on children's production of coherent, emotion and belief-based, narratives in 120 children aged 5 to 10 years. Children were shown a sequence of five wordless pictures depicting a story based on a misunderstanding between two characters, and were requested to narrate the story when the pictures were not present anymore (first narrative). Then, half of the children were asked to explain the reasons behind the events, probing their comprehension of the story, while the other half played a Memory game with the story pictures and their variants. All children were then asked to recount the story (second narrative). Results show that many of the children who participated in the conversational procedure produced second narratives that were more coherent than their first narratives, with events explained by the emotional and belief states of the characters. Instead, no significant changes between first and second narratives occurred in the control group. A conversation focused on the motivations behind actor-caused events, leads children to improve their expression of emotional and belief states of the characters. The discussion will focus on the cognitive processes that are solicited by interventions procedures: changes in executive functions or in deeper-seated developmental constructions?
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".