Evaluating Bilingual Children’s Native Language Abilities in Côte d’Ivoire: Introducing the Ivorian Children’s Language Assessment Toolkit for Attié, Abidji, and Baoulé
Bibliographic record
Abstract
Abstract Few standardized language assessments are adapted to different cultural and linguistic contexts to assess children’s first language (L1) abilities. We introduce the Ivorian Children’s Language Assessment Toolkit for measuring phonological awareness, vocabulary, oral comprehension, and tone awareness in the Abidji, Attié, and Baoulé languages of Côte d’Ivoire. Six hundred and three primary-school children (age 4–14) completed language assessments in their L1 and French. The toolkit provided a reliable and comprehensive assessment of children’s language abilities. We observed age- and grade-related increases in all subtest scores. Still, children scored higher in their L1 compared to French, highlighting the need for language assessments in a bilingual’s two languages to achieve an accurate measure of children’s language abilities. The ability to benchmark children’s scores relative to age- and grade-norms are discussed in the context of language of instruction education policies as well as the potential use of age- and grade-norms in identifying children with language impairment and/or children who are at risk for reading difficulties due to poor language skills.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".