Demonstrative Pronouns in English and Arabic: Are they Different or Similar?
Bibliographic record
Abstract
The present study is a contrastive analysis that delves into the demonstratives in Arabic (Standard Arabic) and English. The aim of the study is to reveal the similarities and differences between the demonstratives of the two languages by delineating their phonological, morphological, syntactic, and semantic properties. Exposing these differences will specify what language teachers have to teach and what language learners whether Arabic or English learners have to learn. The interesting point found is that both Arabic and English demonstratives share more linguistic similarities than differences. Regarding similarities, both demonstratives are indeclinable in both languages except for the Arabic dual case. The demonstratives’ phonemes and their referents are in some way obtained in both languages. English and Arabic languages use demonstratives in several positions within a sentence in consistent with the syntactic function of the demonstrative. Demonstratives in both languages are ambiguous words; their meaning can be defined through their context. As for the differences, English has only two-dimensional deictic points for demonstratives i.e., proximal or distal, but Arabic displays more deictic points i.e., proximal, medial, and distal.
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 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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.003 |
| 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".