Sparkle in the Narratives of Kindergarteners
Bibliographic record
Abstract
Narratives play a significant role in communication and social interaction starting in early childhood. Narrative skills are also expected of children beginning in kindergarten and predict later literacy skills and academic outcomes. While much research has been devoted to the structure of children's stories, less attention has been devoted to their artfulness: the features of stories that make them sparkle and engage listeners. This study investigates how French-speaking children use artfulness in their narratives and how artfulness relates to language skill. Children (N = 91) in Quebec from two different kindergarten levels (K4 and K5, reflecting the age of kindergarten entry) were asked to tell a story using picture prompts from a published assessment tool. The stories were audiorecorded and transcribed, then coded for artfulness features (namely evaluation, appendages, and orientations), using a coding system adapted from Ukrainetz et al. (2005). The results showed that artfulness features, particularly evaluations, were used by all children. The older children (K5) included more artfulness features than the younger ones (K4), overall and within the subcategories of evaluation and appendages. The types of evaluations were also more varied in the older group. General language measures (the number and mean length of T-units, and the number of total and different words) were strongly correlated with the total number of artfulness features in the children's stories and the number and diversity of evaluations they included. I situate my results in relation to the rare studies of artfulness to date, discuss the implications of the findings, and outline directions for future research.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".