Language expansion in Chinese parent–child mealtime conversations: across different conversational types and initiators
Bibliographic record
Abstract
This study examined the distribution of language expansion in parent–child (preschool aged) mealtime conversations in 30 Chinese middle-class families. The conversations were categorised into four types: contextualised & conflicted, contextualised & non-conflicted, decontextualised & conflicted, and decontextualised & non-conflicted. The language expansions were analysed using the systemic functional linguistic theory related to cohesive patterns in language expansion: elaborations, extensions, and enhancements. While the parents dominated the conversations generally, the children were active contributors, initiating over one-quarter of the conversations. Initiation had an impact on the distribution of the conversational types: the proportions of contextualised & non-conflicted conversations was significantly higher in child-initiated conversations. The contextualised & conflicted conversations accounted for a higher proportion in parent-initiated conversations. It was the conversational type rather than initiation, which had an effect on the distribution of language expansion patterns. The least occurring decontextualised & conflicted conversations generated the most extensions. The frequently appeared contextualised & non-conflicted conversations, however, produced the fewest expanded messages. The implications from the findings for promoting high-quality mealtime conversations conducive to children’s language learning are discussed.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".