The Problems of Iranian Language Learners in Correctly Using German Prepositions
Bibliographic record
Abstract
<p>Due to their various types and forms in different languages, prepositions are assumed as a difficult issue in teaching foreign languages. Thus the authors of the present article carried out a field test with the goal of analyzing the use of prepositions by elementary learners of German. The findings of the test confirm the problems students have in using German prepositions. This article is an attempt to compare Persian and German prepositions. The comparison reveals that prepositions in two languages are in no one-to-one relationship with each other. The findings of this research show that German and Persian prepositions are different on three levels: 1. Meaning 2. Grammatical function 3. Sentential position. Furthermore, German prepositions are so various in form and content that it is almost impossible for a learner to make correspondence between the prepositions of the two systems.</p>
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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 | 0.000 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".