The Effect of the Positive and the Negative Evidences in Learning English ‘to’ with Manner-of-Motion to Goal Constructions by L1 Saudi Arabic Speakers
Bibliographic record
Abstract
The present study investigates the effect of the first language (L1) on learners by using the negative and the positive evidence in the classrooms while teaching English directional prepositions such as ‘to’ and ‘into’. It is assumed that Arabic has two versions of ‘to’. It has the directional interpretation without boundary-crossing which is equivalent to the English ‘to’; whereas, it also denotes a similar interpretation to English directional preposition ‘into’ which is unavailable in Arabic and involves boundary-crossing. The study considers two groups to examine the effect of the overlaps, who are at an intermediate stage of development; the experiment group (E.G.) and the control group (C.G.). The control group is the base to measure the effectiveness of the treatments on the experiment groups’ judgments. Hence, an Acceptability Judgment Task is devised to elicit participants’ judgments on the task items in the pretest and the posttest. Results show clear advantage of the negative evidence in the experiment group’s performance in the posttest in learning ‘to’ with and without boundary-crossing. There is a difference in the experiment group’s performance in the posttest in learning ‘into’ with the boundary-crossing event after receiving the positive evidence. Similarly, a difference was observed in the experiment group’s judgment with those of the control group in the comparison between ‘to’ and ‘into’ with the boundary-crossing event in the posttest.
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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) | 0.001 | 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 | 0.001 | 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".