Bilingual children’s repairs of breakdowns in communication
Bibliographic record
Abstract
This study examined two- (n = 10) and three-year-old (n = 16) French-English bilingual children's repairs of breakdowns in communication that occurred when they did not use the same language as their interlocutor (Language breakdowns) and for other reasons (e.g. inaudible utterance). The children played with an experimenter who used only one language (English or French) during the play session. Each time a child used the other language, the experimenter made up to five requests for clarification, from non-specific (What?) to specific (Can you say that in French/English?). The experimenter also made requests for clarification when breakdowns occurred for other reasons, e.g. the child spoke too softly, produced an ambiguous utterance, etc. Both the two- and three-year-olds were capable of repairing Language breakdowns by switching languages to match that of their experimenter and they avoided this repair strategy when attempting to repair Other breakdowns. Moreover, they switched languages in response to non-specific requests. The results indicate that even two-and-a-half-year-old bilingual children are capable of identifying their language choice as a cause of communication breakdowns and that they can differentiate Language from Other kinds of communication breakdowns.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".