The use of referring expressions in narratives by Mandarin heritage language children and the role of language environment factors in predicting individual differences
Bibliographic record
Abstract
This study investigated the referring expressions used for first mentions of participants and entities in narratives by Mandarin heritage language (HL) and monolingual children. Referring expressions for first mentions in Mandarin comprise lexical, morphological and syntactic devices. Results showed that HL children used less adequate referring expressions for first mentions than the monolinguals, mainly due to overgeneralization of classifiers and lack of vocabulary knowledge. However, HL children did not differ from monolinguals in their use of relative clauses and post-verbal NP placement to mark first mentions. These results suggest that incomplete acquisition of the HL may vary across different linguistic subdomains (Montrul, 2008); specifically, domains requiring a great deal of input to acquire, such as vocabulary and the large repertoire of classifier morphemes, might be more vulnerable in HL speakers than syntax. Mixed modeling analyses revealed that older age of arrival, higher maternal education levels and a rich and diverse Mandarin environment at home predicted stronger narrative outcomes, also pointing to an important role for input in HL acquisition.
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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 | 0.000 |
| 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".