The development of discourse referencing in Cantonese-speaking children
Bibliographic record
Abstract
The ability to make clear reference in connected discourse was examined in children learning Cantonese, a Chinese language where noun phrase constituents, whatever their grammatical role, are omissible from sentences under discourse conditions that are not well-understood. Forty-three typically developing children aged 3 ; 0, 5 ; 0, 7 ; 0 and 12 ; 0 told 16 stories based on picture sequences. A panel of adult native Cantonese speakers was asked to judge the referential adequacy of each child's stories by identifying the character the child was talking about in 32 targeted referential acts. The targeted acts were of three sorts: MAINTENANCE of a known character, INTRODUCTION of a second new character, and REINTRODUCTION of a known character. Reference was judged to be adequate when 3 out of 4 'listeners' could successfully identify the character. Children's referential expressions were most adequate for Maintenance, less adequate for Introduction, and least adequate for Reintroduction. The twelve- and seven-year-olds approached ceiling on all three functions. The five-year-olds scored poorly on Reintroduction, and the three-year-olds failed both Introduction and Reintroduction, despite knowledge of at least one of the possible linguistic forms required for these acts as evidenced in a sentence imitation task. Viewed within the framework of Levelt's (1989) discourse model, the data improve our understanding of the developmental period during which children learn to make appropriate presuppositions about the listener's knowledge and attentional states.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".