The acquisition of articles in child second language English: fluctuation, transfer or both?
Bibliographic record
Abstract
The data for this study consisted of a longitudinal corpus of narratives from 17 English second language (L2) children, mean age of 5;4 years at the outset, with first languages (Lls) that do not have definite/indefinite articles (Chinese, Korean and Japanese) and Lls that do have article systems (Spanish, Romanian and Arabic). We examined these children's acquisition of articles in order to determine the role of L1 transfer and, in so doing, test the Fluctuation Hypothesis, and also to compare our findings to those from research on adult L2 learners. Three tendencies were found over two years: (1) All children substituted the for a in indefinite specific contexts (i.e. showed fluctuation) regardless of L1 background; (2) all children were more accurate with use of the in definite contexts than with a in indefinite contexts, regardless of L1 background; and (3) children with [-article] Lls had more omitted articles as error forms than children with [+article] L1s, but only at the early stages of acquisition. Overall, L1 influence in the children's developmental patterns and rates of article acquisition was limited. Child L2 learners converged on the target system faster than prior reports have indicated for adult L2 learners, even when their Lls lack articles. Thus, we conclude that fluctuation is a developmental process that overrides transfer in child L2 acquisition of English articles, in contrast to what has been reported for adult L2 learners.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| 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".