Linguistic Variation in Animal-Sound Word Development: The Cross-Linguistic Examination of Common Animal-Sound Words
Bibliographic record
Abstract
The goal of this study is to investigate the cross-linguistic differences in the development of animal-sound word production in children between the ages of 12-30 months across 17 languages. Specifically, I examined whether children’s ability to produce animal-sound words are impacted by factors such as age, gender, language, and syllable length. Previous studies have displayed that animal-sound words (e.g., woof woof) were among the early words of monolingual English-speaking children. However, there is little known about how the development of animal-sound words may differ in other languages. Due to linguistic exposure to animals, familial influences, and other variables impacting the child, it is likely that there are cross-linguistic differences in the production of animal-sound words. Using MacArthur-Bates Communicative Development Inventories (MB-CDIs) data from Stanford's Word Bank, I examined children’s animal-sound word production across 17 languages: English, Cantonese, Croatian, Danish, French, Greek, Hebrew, Italian, Kiswahili, Korean, Norwegian, Mandarin, Russian, Slovack, Spanish (Mexican), Swedish, and Turkish. For the English language, we included data from children speaking American English, Australian English, British English. For French, we included data from children speaking French as well as French (Quebecois). A total of 17,409 participants (8,199 girls, 8,091 boys, and 135 not reported), ranging between 12-30 months, were included in this dataset. Specifically, this study included the analysis of seven animal-sound words: cockadoodledoo (rooster), baa baa (sheep), meow (cat), moo (cow), quack quack (duck), grr (bear), and woof woof (dog). Results show that children’s age, gender, language, and syllable length are all significant predictors of the development of animal-sound words. The findings suggest that there are differences across the languages and through the patterns of animal-sound word production cross-linguistically. Clinical implications are discussed.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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".