Structural Development of Narratives in Arabic: Task Complexity, Age, and Cultural Factors
Bibliographic record
Abstract
Purpose This study examines the effect of age and task complexity on the macrostructure of story production in preschool- and school-age Kuwaiti Arabic-speaking children. It also compares the children's production of core and complementary macrostructure story elements. Method A descriptive, cross-sectional research design was used to explore the participants' narrative skills. A total of 122 monolingual speakers of Kuwaiti Arabic (97 children and 25 adults) participated in this study. The children aged 4;0 to 7;11 (years;months) were randomly recruited from public schools across Kuwait. There were 24 four-year-olds (Kindergarten 1), 23 five-year-olds (Kindergarten 2), 23 six-year-olds (Grade 1), and 27 seven-year-olds (Grade 2). A group of adults was also included to establish a benchmark. Storytelling was elicited from all the participants using two sets of sequential pictures from the Edmonton Narrative Norms Instrument: a one-episode story and a more complex three-episode story (Schneider et al., 2005). Across-group comparisons were conducted to explore the effect of age, story complexity, and type of macrostructure elements on story production. Results The findings revealed a progression by age in the development of story macrostructure, but there was no effect of task complexity. Within all age groups, the core macrostructure components were mastered before the complementary elements. Conclusions The results of this study confirmed that cross-linguistic narrative measures could be used in contexts that are culturally and linguistically different with minor adaptations. The piloting of two picture-based stories showed that the shorter one-episode version may be sufficient to evaluate the language development of this age group.
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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 | 0.012 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".