Crosslinguistic transfer in the acquisition of compound words in Persian–English bilinguals
Bibliographic record
Abstract
Crosslinguistic transfer in bilingual language acquisition has been widely reported in various linguistic domains (e.g., Döpke, 1998; Nicoladis, 1999; Paradis, 2001). In this study we examined structural overlap (Döpke, 2000; Müller and Hulk, 2001) and dominance (Yip and Matthews, 2000) as explanatory factors for crosslinguistic transfer in Persian–English bilingual children's production of novel compound words. Nineteen Persian monolinguals, sixteen Persian–English bilinguals, and seventeen English monolinguals participated in a novel compound production task. Our results showed crosslinguistic influence of Persian on English and of English on Persian. Bilingual children produced more right-headed compounds in Persian, compared with Persian monolinguals, and in their English task, they produced more left-headed compounds than English monolinguals. Furthermore, Persian-dominant bilinguals tended more towards left-headed compounds in Persian than the English-dominant group. These findings point to both structural overlap and language dominance as factors underlying crosslinguistic transfer.
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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 | 0.000 | 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 | 0.000 |
| Scholarly communication | 0.000 | 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".