Continued Catch-Up and Language Delay in Children Adopted from China
Bibliographic record
Abstract
Most internationally adopted children learn their new language rapidly after adoption. What is not known is whether these gains continue at the same developmental pace throughout the preschool years. To answer this question, the continued language development of the 10 lowest performers from a cohort of 55 preschool-aged children adopted from China was examined. All 55 children had originally been assessed approximately 2 years earlier as preschoolers. The purpose was to examine whether continued English language exposure resulted in greater gain scores on a battery of standardized speech-language tests normed on monolingual English speakers. The 10 lowest performers were retested on the same battery approximately 2 years later. Scores on the second testing were examined in two ways. First, the amount of gain made from first to second testing for each child was examined, and second, the low performers were compared with adopted children from the original cohort who were matched for age and duration of time in the United States. It was found that more than half of the low-scoring children made clinically significant gains on the second assessment, but that low performers as a group scored below the level of matched peers in the cohort, even after approximately 2 years of additional English language exposure.
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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 | 0.001 |
| 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 | 0.000 |
| 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".