Early Bilingual Vocabulary Development Among Low-SES Ethnic Minority Learners in China: The Case of Uyghur and Kazak Children
Bibliographic record
Abstract
Abstract Vocabulary knowledge is one of the most important aspects of language development. For bilingual students, early vocabulary development often predicts their future bilingual success. This paper examines early bilingual receptive vocabulary knowledge of ethnic minority children (N=135) from two large ethnic language communities (Uyghur and Kazak) in three national-level poverty-stricken counties in Xinjiang, China. The children’s bilingual vocabulary knowledge was assessed using translated versions of the Peabody Picture Vocabulary Test-IV (PPTV-IV) in Putonghua (PTH) and their mother tongue (MT) Uyghur or Kazak. Data were analyzed through four General Linear Models (GLM). The analyses showed that both groups scored higher in MT vocabulary knowledge than that in their PTH, although the Kazak students’ MT vocabulary scores were lower than those of the Uyghurs. While gender, age, L1, or residence location were not significant factors in differences across the two groups in PTH, among the Kazak children, the main effect of age was significant in MT; and among Uyghur children, residence location had a significant effect. The two groups also differed in patterns of acquisition in different parts of speech (nouns, verbs, and attributes) with Uyghur children performing strongest in MT and PTH verbs. The findings have important implications for ensuring the quality of early bilingual education among impoverished Chinese minority communities.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 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".