Contrastive View Between Several English and Albanian Prepositions
Bibliographic record
Abstract
This paper presents a contrastive view on use of several English and Albanian prepositions, with particular focus on several English prepositions, in, on and at, equivalent to Albanian preposition në. In English language, grammatically there are clearly defined uses of these prepositions. However, in Albanian language, all three English prepositions are translated with Albanian preposition në, which shows that Albanian language is less developed when it comes to the use of prepositions. This is due to the development of cases system and word endings based on internal grammatical rules of Albanian language. Also, in this study there will be presented some other contrasts between English and Albanian prepositions, in regards to, classification of prepositions according to cases in Albanian language: prepositions in nominative, genitive, accusative and ablative (Alb. rrjedhore) case and lack of their classification in cases in English grammars; conversion of prepositions into conjunctions in some cases in English and lack of this occurrence in Albanian; use of prepositions in the end of sentence in English and lack of this occurrence in Albanian, since it is in contradiction with internal syntax rules of Albanian language.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.002 |
| 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".