Articles in child L2 English: When L1 and L2 acquisition meet at the interface
Bibliographic record
Abstract
In this study, the authors investigate the acquisition of the article system of English as a phenomenon at the interface between morphosyntax and semantics. L1 acquisition studies have found that children make mistakes in article use until they are at least four years old or possibly older. Also, adult L2 acquisition studies have reported that learners of English often have consistent difficulty in the use of articles until very late stages of acquisition. This study sought to understand whether child L2 learners would display acquisition patterns similar to child L1 for the English article system. The authors analyzed article use in L2 children from four L1 backgrounds: Mandarin/Cantonese Chinese, Hindi/Urdu/Punjabi, Arabic, and Spanish. The findings of the study indicate that the interface domain of the article system is indeed problematic for child L2 learners. The authors found that all L1 groups had difficulty acquiring the semantic aspect of the phenomenon. In the no-article L1 groups, the acquisition of the morphosyntactic aspect of article use showed the effect of L1 in the form of article omissions. Transfer of the mapping of the feature [−definite] onto indefinite article forms from L1s did not take place in the Arabic and Spanish L1 groups, indicating that L1 transfer in child L2 acquisition is limited. Comparing the findings with those of the previous studies of child L1 and adult L2 acquisition, the authors conclude that the predominant trends in children’s article acquisition were developmental rather than transfer-based. This finding in particular highlights the special status of L2 children as a unique learner population.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".