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Record W4360976992 · doi:10.5430/wjel.v13n2p537

Contrastive View Between Several English and Albanian Prepositions

2023· article· en· W4360976992 on OpenAlexvenueno aff
Fridrik Dulaj, Petrit Duraj, Shemsi Haziri, Senad Neziri

Bibliographic record

VenueWorld Journal of English Language · 2023
Typearticle
Languageen
FieldArts and Humanities
TopicLinguistics, Language Diversity, and Identity
Canadian institutionsnot available
Fundersnot available
KeywordsGenitive caseComputer scienceLinguisticsNominative caseFocus (optics)SyntaxSentenceNatural language processingContrastive analysisArtificial intelligenceVerbNounPhilosophy

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.442
Threshold uncertainty score0.723

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.016
GPT teacher head0.236
Teacher spread0.219 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2023
Admission routes1
Has abstractyes

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