Phonological Skills of Children Adopted from China: Implications for Assessment
Bibliographic record
Abstract
Little is known about the acquisition of English phonology by children adopted from China. Data are summarized from three recent studies with a focus on the phonological skills of children adopted from China as infants or toddlers. Two longitudinal studies (combined n = 8) described early phonological behaviors (e.g., babbling, phonetic inventories), and found substantial individual variation. In spite of this variation by 3 years of age, nearly all of the children were performing at a level comparable to nonadopted monolingual English-speaking peers. No clear relationship between the early behaviors and outcome at age 3 was found. The third study provided descriptions of the phonological skills of preschoolers ( n = 25) who had been adopted 2 or more years earlier, and found that only a few had persistent phonological delays. Errors were predominantly common developmental errors frequently observed in nonadopted monolingual English-speaking children. These findings suggest that tests and measures developed for monolingual English-speaking children may be used cautiously with children adopted as infants or toddlers who have been in their permanent homes for 2 or more years. Prior to that time, assessment should focus on independent analyses of phonological behaviors with consideration of the child's chronological age, length of exposure to English, and development in other language domains.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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".