Syntactic awareness skills in English among children who speak Slavic or Chinese languages as a first language and English as a second language
Bibliographic record
Abstract
Aims and objectives/purpose/research questions: The purpose of this study was to examine syntactic awareness skills in English, among two groups of children: native Chinese speakers and native Slavic (L1) speakers. Both groups were second language English (L2) speakers. Their syntactic awareness skills were compared to a matched sample of L1 English speakers. Design/methodology/approach: Eighty-six third grade students participated in the study, matched between language groups on the basis of age and gender, as well as academic achievements and word reading levels. Data and analysis: L1 English and L1 Slavic groups performed significantly better on the syntactic awareness task than did the L1 Chinese group. A close examination of specific syntactic constructions revealed that the L1 Chinese group did not perform as well as the other groups on past tense constructions, which do not exist in Chinese but do exist in Slavic languages. However, there were no between-group differences on superlative and comparative constructions, which exist in all three languages. Findings/conclusions: The results contribute to our knowledge about cross-linguistic influences between English, Slavic, and Chinese, showing that L1 Slavic facilitates the learnability of L2 English, while L1 Chinese impedes the learnability of L2 English. Originality: The originality of the study lies in the comparison of children from three different L1 groups, matched with respect to reading level. The examination of languages that are typologically different in their syntax is unique. Significance/implications: The results highlight the importance of taking the specific language backgrounds of L2 learners into consideration. Limitations: The current study did not include an assessment of L1 language proficiency among participants.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.002 | 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".