Bibliographic record
Abstract
The present study investigated whether the presence of an older sibling affects the language secondborn children hear. In particular, we investigated whether mothers' distribution of language across the three functional categories of metalingual, referential and social-regulative would differ from a mother-child dyadic to a mother-child-sibling triadic context, in support of Nelson's (1981) hypothesis. In addition, we investigated how older siblings' speech to both the child and the mother in the triadic context contributes to the linguistic environment of the secondborn children. Fourteen English-speaking secondborn children were videotaped at 21 months of age in two 25-minute free-play sessions, one with their mothers and the other with their mothers and older siblings. Mothers' and older siblings' utterances were analysed in terms of three function categories. The results provided evidence for Nelson's hypothesis that in the triadic context, mothers used more language centred around children's activities and social exchanges (social-regulative language), whereas in the dyadic context, they used more language-focused language (meta-lingual language). Furthermore, older siblings' utterances to the secondborn in the triadic context were overwhelmingly social-regulative, whereas their utterances to the mother were more metalingual and referential. These results suggest that linguistic environment of secondborn children is qualitatively different from that of firstborn children.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".