Involuntary Syntactic Error of Interlingual Word Order When English Rigid Word Order Momentarily Deforms Arabic Clauses
Bibliographic record
Abstract
This paper intends to explore the potential momentary influence of English rigid word order on the placement of Arabic preverbal subjects. The idea is that English as a Subject-Verb-Object language has one subject position; thus, it poses no restrictions on the distribution of determiner phrases in this position. By contrast, Standard Arabic (henceforth, Arabic) uses two different word orders (Subject-Verb/Verb-Subject-(Object), SVO/VSO). As a result, indefinite determiner phrases are not freely distributed in the subject position; that is, they can appear in the postverbal subject position -VSO but not in the preverbal subject position -SVO. Because the two languages use different syntactic word orders and different subject positions, two experimental tasks (an Arabic guided writing task and an English-to-Arabic translation written task) were administered to find out whether the English word order momentarily causes Arabic learners of English to violate their language subject distributions. Analysis of the performance of Arabic native participants in the two tasks revealed two important outcomes: a) when participants were asked to reorder scrambled words into full clauses, they significantly preferred VSO order; in contrast, b) when participants were asked to translate full English clauses into Arabic, they strikingly preferred SVO order violating syntactic parametric (distributional) restrictions on the placement of indefinite determiner phrases. In other words, they used indefinite determiner phrases in the preverbal subject position. Based on the results, the study argues that the improper use of indefinite determiner phrases in the preverbal subject position is not due to the implicit knowledge of Arabic grammar; it is due to the momentary influence of English syntactic word order involuntarily exerted by participants.
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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 | 0.009 |
| 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".