French object clitics in sequential versus simultaneous bilingual acquisition
Bibliographic record
Abstract
In this short exploratory study we investigate the production of French object clitics in sequential bilingual acquisition, and determine whether the observed patterns are similar to those in simultaneous bilingual acquisition. A picture elicitation task was used with 11 Anglophone children learning French (mean age 4;09) and 11 age-matched English-French simultaneous bilinguals, as well as 11 aged-matched monolingual French children. No errors in clitic placement were found in the sequential group, in line with previous results from older children. In both bilingual groups, the majority of responses were null objects (more than 60%). It appeared that children with a later age of onset and shorter exposure to French did in fact perform similarly to bilingual children who acquired French from birth. The high number of object omissions suggests a quantitative effect when compared to monolingual Francophone children. We propose that this delay is due to the retention of a default null object representation.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.003 | 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".