Why is “John Ran to the House” the Same as “John Went to the House Running” in Arabic?
Bibliographic record
Abstract
The study explores how Arabic has the same conflation pattern characteristics as English even though it belongs to Verb-framed Languages. A focused-group approach is used to evaluate the effect of the first language (L1) and the potential role of proficiency in the acquisition of the English directional preposition ‘to’ with manner-of-motion to goal construction. One group consists of Saudi speakers at two levels of development; an intermediate and advanced proficiency levels; whereas, the second group (control group) comprises of English native speakers. Acceptability Judgment Task associated with video animation clips is designed to elicit participants’ judgments in the depicted event. Results indicated that the intermediate Saudi speakers accept the directional preposition ‘to’ with and without boundary-crossing event, as is the case of their L1, which was opposite for the advanced and native English speakers for the without boundary-crossing event. The advanced Saudi speakers accept the constructions of encoding the manner with the motion and expressing the manner as the complement depicting an appropriate description of the event, reflecting L1 influence. All the group’s judgment varies based on the acceptance to conflate the manner with the motion overexpressing manner as a complement in an event without boundary-crossing.
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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 | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".