Study on Lexical Cohesion in English and Persian Research Articles (A Comparative Study)
Bibliographic record
Abstract
The present study aims to analyze comparatively English and Persian research articles (Linguistics, Literature, and Library and Information disciplines) in terms of number and degree of utilization of sub-types of lexical cohesion in order to appreciate textualization processes in the two languages concerned. The study analyzes 60 research articles (30 articles in each language) in terms of sub-types of lexical cohesion. The study reveals that the order of occurrence in descending order of sub-types of lexical cohesion is ( Rep., Col., Syn., Gen.N., Mer., Hyp., and Ant.) in English data, while the order in Persian data is ( Rep., Syn., Col., Ant., Hyp., Mer., Gen.N.). In both data the most frequent sub-types are repetition, collocation, synonymy. In English data the general tendency is towards the use of repetition and collocation but Persian data show the general tendency towards the use of repetition and synonymy. This study might have implications for teachers and researchers in the field of teaching English as a foreign language because of the fact that teaching sub-types of lexical cohesion to foreign language learners will improve the quality of their reading and writing.
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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 | 0.005 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.019 | 0.017 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".