Using semantic verbal fluency to estimate the relative and absolute vocabulary size of bilinguals: An exploratory study of children and adolescents
Bibliographic record
Abstract
INTRODUCTION: The full assessment of bilingual children often involves at least one language for which formal vocabulary tests are lacking and which the examiner does not speak. We examined, in a sample of children with typical development (TD), whether a semantic verbal fluency task, typically used in research as a measure of executive function, could be used in the place of a formal vocabulary test to estimate vocabulary knowledge when formal tests are not available. METHOD: 113 TD monolingual French speakers and TD bilinguals and with varying degrees of exposure to French, age 6 to 17 years, completed tests of vocabulary knowledge and semantic verbal fluency. A subset of 64 participants spoke French and English and were tested in both languages. Verbal fluency measures calculated using a traditional method which uses specific rules for superordinate categories and for animals of different sex and age and a simplified scoring method which simply counts all words produced, included the total number of words produced in each language, Total Vocabulary and Conceptual Vocabulary measures combining both languages, as well as analyses of lexical composition and word frequency within the study sample. RESULTS: Linear regressions revealed that the number of words produced predicted vocabulary size in a language-specific way, with slightly stronger predictions made by the simplified scoring method. As expected, bilinguals produced more words and more unique words in their language of greater exposure, while different exposure groups were equivalent in measures combining both languages, including their Total vocabulary and Conceptual vocabulary. Producing unusual words (infrequently produced in the study sample) indicated higher vocabulary scores. CONCLUSIONS: This study supports the use of the verbal fluency task as a quick and simple tool to obtain a rough estimate of vocabulary size in TD monolinguals and bilinguals. This tool shows promise as well in clinical work with other populations, subject to further verification.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".