The Role of L2 Input in the Acquisition of English Non-Pleonastic Constructions to Reset L1 Parameters by Saudi Arabic Speakers
Bibliographic record
Abstract
This study examines the acquisition of non-pleonastic English constructions by first-language (L1) Saudi Arabic speakers in second language (L2) English comprising two types of pleonasm: acronym pleonasms and semantics pleonasms. It is known that Arabic speakers tend to use redundant expressions in their native language to emphasize their ideas or clarify foreign terms. This study focuses on whether advanced Saudi Arabic speakers can reset their L1 parameters in the final state of English non-pleonastic construction acquisition. The acceptability judgment task was devised to elicit participants’ judgments on these two types of pleonasms. Two groups joined the study: advanced Saudi Arabic speakers as the experiment group (n=40) and native English speakers as the control group (n=32). The experiment’s results suggested that the advanced Saudi Arabic speakers failed to reset the L1 parameters in their judgments on acronym pleonasms. However, the findings showed the Saudi Arabic speakers’ performance resembled that of the native English speakers with respect to semantics pleonasms. This study reveals that Saudi native speakers transfer their L1 properties in their L2 acquisition and fail to access UG to restructure their grammar because they lack extensive exposure to the L2 input of these constructions. The study supports the full transfer hypothesis in the acquisition of L2 constructions. In conclusion, this study provides valuable recommendations to the educational system in Saudi Arabia for implementing these constructions in curricula to enhance L2 input.
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.
How this classification was reachedexpand
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".