L1 grammatical attrition through the acquisition of competing L2 discourse features
Bibliographic record
Abstract
A question in language acquisition research is whether attrition can affect L1 grammatical representation, and if so, under what conditions. This paper tests the Attrition via Acquisition (AvA) model, which takes a Feature Reassembly approach to predict how, in case on high degrees of similarity between the L1 and L2, the acquisition of L2 discourse-driven morpho-syntactic properties may affect L1 feature representations after a prolonged change in the speaker’s primary linguistic input during adulthood. As a test case, we use the different features (specificity versus discourse anaphoricity) associated with Clitic Left Dislocation (CLLD) in Romanian and Italian, examining the grammars of Romanian first-generation immigrants with either L2 Italian or L2 English (a language without CLLD). Using a context-dependent Acceptability Judgment task and a Written Elicitation task we found evidence for L2-induced grammatical attrition, resulting in the addition of an L2 option without the loss of an L1 option, as predicted by the AvA. Attrition was found for participants who immigrated during adolescence or early adulthood and who are more likely to consider Italian their most proficient and most used language. We compare our findings on attrited L1 grammars to the results of a recent study reporting on near-native L2 Italian and L2 Romanian grammars by Romanian and Italian native speakers. Our findings contribute to an increasing body of literature showing that L1 attriters and L2 learners can end up with very similar grammars and confirm the importance of studying second language acquisition and L1 loss within a broader picture of bilingual development.
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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 | 0.003 | 0.014 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".