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Record W2574701250 · doi:10.5539/ijel.v7n1p94

Comparison between the Characteristics of Inflectional Systems in Arabic and English Languages

2017· article· en· W2574701250 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueInternational Journal of English Linguistics · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicArabic Language Education Studies
Canadian institutionsnot available
Fundersnot available
KeywordsComputer scienceLinguisticsArabicNounPronunciationNatural language processingHabitArtificial intelligencePsychologyPhilosophy

Abstract

fetched live from OpenAlex

The study has contributed to compare the characteristics of the inflectional system of nouns in Arabic and English Language for pedagogical orientation. The main objective was to develop an overview of the two languages system for evaluating the pedagogical significance in Arabic and English. A qualitative research design has been implemented to introduce the nature of two languages and the anticipated difficulties that have been encountered by the learners of the Arabic and English languages. The classification of the nouns and the inflectional system of the two languages have been contrasted through the analysis. It has been observed that the Arab learners of English delete some letters while writing but have proper pronunciation because they have the habit of pronouncing two elements represented in one grapheme.

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.

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)0.001
metaresearch head score (Gemma)0.169
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.240
Threshold uncertainty score0.838

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.169
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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.040
GPT teacher head0.400
Teacher spread0.360 · 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