Effects of Input Properties, Vocabulary Size, and L1 on the Development of Third Person Singular –<i>s</i> in Child L2 English
Bibliographic record
Abstract
This study was designed to investigate the development of third‐person singular (3SG) – s in children who learn English as a second language (L2). Adopting the usage‐based perspective on the learning of inflection, we analyzed spontaneous speech samples collected from 15 English L2 children who were followed over a 2‐year period. Assessing the contribution of a wide range of predictors, we show that word frequency, allomorph, lexicon size, inflectional properties of the first language (L1), and months of exposure to English all have impact on English L2 children's use of 3SG – s in obligatory contexts. This study enhances both our understanding of the development of 3SG – s and of child L2 acquisition. The outcomes support a usage‐based approach to learning inflection and emphasize the importance of a multifactorial analysis of language development.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 |
| 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".