The acquisition of object movement in Dutch: L1 transfer and near-native grammars at the syntax–discourse interface
Bibliographic record
Abstract
This article investigates near-native grammars at the syntax–discourse interface by examining the second language (L2) acquisition of two different domains of object movement in Dutch, which exhibit syntax–discourse or syntax–semantics level properties. English and German near-native speakers of Dutch, where German but not English allows the same mapping strategies as Dutch in the phenomena under investigation, are tested on two felicity judgment tasks and a truth value judgment task. The results from the English participants show sensitivity to discourse information on the acceptability of non-canonical word orders, but only when the relevant discourse cues are sufficiently salient in the input. The acquisition of semantic effects on object movement was native-like for a large subset of the participants. The German group performed on target in all experiments. The results are partially in line with previous studies reporting L2 convergence at the syntax–discourse interface, but suggest that input effects should also be taken into account. Furthermore, the differences between the first language (L1) English and the L1 German group suggests that non-target performance at the syntax–discourse interface is not caused by general bilingual difficulties in integrating discourse information into syntax. The article elaborates on factors that contribute to (in)complete acquisition at the syntax–discourse interface.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".