<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
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 it