Language abilities of internationally adopted children from China during the early school years: Evidence for early age effects?
Bibliographic record
Abstract
ABSTRACT We assessed the language, cognitive, and socioemotional abilities of 27 internationally adopted children from China, adopted by French-speaking parents, 12 of whom had been assessed previously by Gauthier and Genesee. The children were on average 7 years, 10 months old and were matched to nonadopted monolingual French-speaking children on age, gender, and socioeconomic status. Although there were no significant differences between the groups with respect to socioemotional and cognitive development, the adoptees scored significantly lower than the controls on measures of receptive grammar, expressive vocabulary, word definitions, and sentence recall, findings that were similar to those reported by Gauthier and Genesee. Analyses of correlations between the adopted children's language test results and their age at adoption, length of exposure to the adoption language, health, and other developmental problems revealed relatively few significant associations. In contrast, analyses of the relationship between their language test scores and their performance on the recalling sentences subtest suggest a link between performance on these two tests. We speculate on the role that performance on sentence recall might play in mediating differences in language outcomes between the two groups of 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".