Presupposition Use in Arabic Political Discourse: The Case of King Salman Speech on Terrorism
Bibliographic record
Abstract
This paper aims at investigating presupposition use in Arabic political discourse. The study attempts to answer the feasibility of using presupposition as a convincing tool in Arabic political discourse. The study adopts the Accommodation Analysis model, as examples from the speech of Saudi King Salman bin Abdulaziz on terrorism in 2017 are analyzed from two perspectives: Speaker presupposition perspective and Utterance presupposition perspective. The analysis found that using the Speaker presupposition perspective, presuppositions can pass unblocked, and when a plug exists, the local context creates a hole so that the presupposition can be accommodated successfully. The analysis stresses the value of context to accommodate presuppositions when they encounter projection problems. Presuppositions are more likely blocked when the Utterance presupposition perspective is adopted because different groups may have different interpretations. The findings of the study stress that sharing knowledge, i.e., political beliefs in the context of the study, is vital for a presupposition to pass unblocked.
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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".