Feature reassembly and L1 preemption: Acquiring CLLD in L2 Italian and L2 Romanian
Bibliographic record
Abstract
This study investigates feature acquisition and feature reassembly associated with Clitic Left Dislocation (CLLD). The article compares the acquisition of CLLD in second language (L2) Italian to L2 Romanian to examine effects of first language (L1) transfer, construction frequency and the type of interface involved (external vs. internal interface) within the same syntactic construction. The results from an acceptability judgment task and a written elicitation task show that while English near-native speakers of Italian/Romanian acquired the L2 constraints on CLLD, which is [+anaphor] for Italian and [+specific] for Romanian, data from both Romanian L2 learners of Italian and Italian L2 learners of Romanian showed persistent L1 transfer effects. Target-like acquisition for these groups requires both grammatical expansion and retraction; Romanian CLLD requires the addition of an L1-unavailable [+specific] feature and the loss of a [+anaphor] feature, while Italian CLLD requires the addition of an L1-unavailable [+anaphor] and the loss of a [+specific] feature. The reported findings extend evidence in favour of the Feature Reassembly Hypothesis to the syntax-discourse interface, as reassembly of interpretational features associated with CLLD proved more difficult than feature acquisition. While learners at the near-native levels were able to broaden the contexts that allow a clitic in the L2 (grammatical expansion), L1 preemption difficulties were attested as well. This was the case regardless of the frequency of the relevant construction in the input and the type of L2 feature that needed to be added/removed.
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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 | 0.003 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".