Acquisition of complement clitics and tense morphology in internationally adopted children acquiring French
Bibliographic record
Abstract
The present study examined the language development of children adopted from China to examine possible early age effects with respect to their use of complement clitics, lexical diversity and verb morphology. We focused on these aspects of French because they distinguish second language learners of French and native French-speaking children with language impairment from children learning French as a native language and, in the case of object clitics and certain verb tenses, are relatively late to emerge in native speakers. Thus, it might be expected that they would be susceptible to the delayed onset of acquisition of French experienced by internationally adopted children. Language samples of twelve adopted children from 3;6 to 4;8 living in French-speaking families were analyzed and compared to those of non-adopted monolingual French-speaking children of the same age, sex and socio-economic status. The adopted and control children had similar levels of socio-emotional adjustment and non-verbal intellectual abilities. The adopted children exhibited accelerated language development in general, and there were no significant differences between the internationally adopted and control children with respect to lexical diversity and verb tense. However, the adopted children made significantly more errors using complement clitics, and in particular object clitics, compared to the non-adopted children. The results are discussed in terms of possible effects related to delayed age of acquisition of French.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".