Narrative microstructure and macrostructure skills in Arabic diglossia: The case of Arab immigrant children in Canada
Bibliographic record
Abstract
Aims and objectives: The study investigated narrative microstructure skills of Arabic-speaking immigrant children in Canada ( N = 75; age range 7–12 years) with specific focus on diglossia and the lexical distance between Spoken Arabic (SpA) and Standard Arabic (StA). The study also tested the relationship between microstructure and macrostructure and probed into the relative importance of general versus diglossia-specific features of microstructure in predicting macrostructure. Design/methodology/approach: Participants were asked to tell a story from a picture using an Arabic version of the Test of Narrative Language (Gillam & Pearson, 2004). Instructions to participants were given in StA. Data and analysis: General measures of microstructure were coded: number of tokens, number of types, type/token ratio, and mean length of utterance (MLU). In addition to these general measures, we coded the average frequency of five diglossia-specific word types: (a) identical words, which keep the same phonological form in StA and SpA; (b) SpA cognates, namely, cognate words that keep different yet related forms in StA and SpA, used in their SpA form; (c) StA cognates, cognate words used in their StA forms; (d) unique SpA words; and (e) unique StA words. Regression analysis was used to predict macrostructure from general and diglossia-specific features of microstructure. Findings/conclusions: Results showed that the bulk of the lexicon of the narratives produced by immigrant children consisted of words and word forms that are within SpA: identical words, SpA cognates, and unique SpA words; StA word forms appeared less frequently, and English code-switched words were very rare. Results also showed that the microstructure features of narrative length in tokens and type/token ratio significantly predicted macrostructure beyond the children’s age and Arabic language proficiency. However, when diglossia-specific lexical features were added as predictors, the frequency of StA words predicted unique variance in macrostructure beyond age, Arabic language proficiency, and narrative length. Findings advance our understanding of narrative skills in Arabic diglossia among new immigrants and the role of lexical distance in narrative production in this context. Originality: The study is innovative in investigating the manifestation of diglossia in narrative microstructure features and the role of diglossia-specific features in predicting macrostructure, as well as in testing this question among immigrant children. Significance/implications: The study demonstrates the multifaceted lexicon of diglossic Arabic speakers as reflected in the microstructure of their narratives and the prevalence of SpA word forms in their lexicons. The study also demonstrates a significant relationship between microstructure and macrostructure, and the important role of StA lexical features of microstructure in predicting macrostructure. The results of the study have theoretical implications for the importance of lexical distance in understanding narrative production in children at both the microstructure and macrostructure levels. The study also has practical implications for assessment and intervention with Arabic-speaking children in diglossic Arabic.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| 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".