Exploring the Acquisition of English Plural Formation and Compounding: Insights from L1 speakers of Libyan Arabic
Bibliographic record
Abstract
One of the most widely studied morphological phenomena in psycholinguistics is the avoidance of regular but not irregular plurals in noun compounds (e.g., rats eater vs. mice eater). This study addresses this issue by examining the acquisition of English synthetic and root compounding by L1 speakers of Libyan Arabic, focusing on the role of L1 transfer and Universal Grammar (UG) in learning this presyntactic property. Specifically, it investigates whether morphological constraints on plural formation in noun compounds are universally available to second language learners or subject to L1 influence. Participants were selected at different phases of learning English in the classroom to offer an indication of possible developmental progress. A forced-choice gap-filling task was used to investigate how learners apply pluralization rules in English compounds. The results suggest some evidence of L1 influence, but no clear indication of UG influence. Moreover, little development change was observed across proficiency levels. These findings challenge the claims that morphological level-ordering is universally and innately accessible (e.g., Clahsen, 1991; Clahsen et al., 1992; Gordon, 1985). Overall, the results are consistent with an L1 transfer/access to UG view of the L2 acquisition of pre-syntactic properties, without providing strong support for this position.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".