Early Language Growth in Children Adopted from China: Preliminary Normative Data
Bibliographic record
Abstract
Normative data on English language development in children adopted from China are needed to determine whether a child's language skills are within normal limits or significantly delayed relative to his or her peers who are other children adopted from China. A longitudinal survey of children adopted from China, modified from a similar survey used by Glennen and Masters (American Journal of Speech-Language Pathology 2002;11:417-433) with children adopted from Eastern Europe, was used to collect information on English language development from over 150 children at 3-month intervals. Preliminary results are presented here, based on 808 surveys from 141 children grouped by age at time of adoption. In general, children adopted at older ages used more words and produced longer sentences at each 3-month interval postadoption, but had further to go to "catch up" to norms for nonadopted monolingual English-speaking peers of the same age. Individual profiles illustrate the variation seen within groups, with some children performing at or above age level and others showing varying levels of "delay" relative to nonadopted monolingual English-speaking peers and/or adopted peers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".