<i>Wh-</i> questions in child L2 French: Derivational complexity and its interactions with L1 properties, length of exposure, age of exposure, and the input
Bibliographic record
Abstract
This study investigates how derivational complexity interacts with first language (L1) properties, second language (L2) input, age of first exposure to the target language, and length of exposure in child L2 acquisition. We compared elicited production of wh-questions in French in two groups of 15 participants each, one with L1 English (mean age 8 years 10 months or 8;10) and one with L1 Dutch (mean age 6;3), which were further subdivided into subgroups matched for the different variables under examination. Although in their L1s wh-questions display wh-movement and subject–verb/aux inversion, the learners did not perform similarly. A high number of wh-in-situ questions (i.e. the least complex option) was produced by the L1-English children, suggesting that derivational complexity can override L1 influence. In the L1-Dutch group, questions with overt wh-movement were more frequent. This may stem from the influence of generalized XP-movement to the left periphery in Dutch. Inversion (i.e. the most complex option) was rare in both groups and was related to contact with formal schooling. These results hold across the different subgroups, which suggests not only that complexity plays a role in child L2 acquisition, but also that its effects may differ according to the properties of the L1.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".