Differences of Style between English and Arabic Political Discourse: A Contrastive Study
Bibliographic record
Abstract
Traditionally, style is defined as the expressive, emotive or aesthetic emphasis added linguistically to the discourse with its meaning is the same. In the current study, however, style is defined as the linguistic choice that the language users can make for specific purposes. This study, thus, aims at analyzing political Arabic and English speeches to find out whether there are differences of style between English and Arabic and whether the choices the language users make can show any traits of their psychological status. To fulfill the above aims, the study hypothesizes that English and Arabic speeches can be analyzed stylistically and that there are stylistic differences between English and Arabic. Two political speeches have been selected to be the data of this paper. The first is an Arabic one by the former Egyptian president Jamal Abdul Nasir. The second is an English one by president David Johnston, governor of Canada. The model adopted here is an eclectic one. Style, according to this model, is defined as the choice the language user made at two axes; direct and indirect. The direct choice is determined relying on the theory of systemic functional grammar. At the indirect axis choice, the traditional approach of style is adopted. The study comes to some conclusions the most important of which is that language users utilize unconsciously linguistic choices to express their points of view and intentions which say a lot about their psychological status and thus their characters.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".