Rhetorical and Persuasive Strategies Employed by Imran Khan in his Victory Speech: A Socio-Political Discourse Analysis
Bibliographic record
Abstract
This study aims to explore the rhetorical and persuasive strategies employed by a political leader to propagate his ideology using language. It intends to critically analyze the victory speech of Pakistani Premier Imran Khan (IK)—the Chairman of Pakistan Tehreek-e-Insaf (PTI)—which he delivered at the Prime Minister House, Islamabad, after being elected as the 22nd Premier of Pakistan in 2018. The researchers attempt to unveil and analyze critically the strategies that worked behind this speech to persuade the audience. Different linguistic tools used for projecting and achieving political power have been identified and scrutinized. The qualitative analysis of the speech is based on theory of Aristotle’s Rhetoric; Ethos, Pathos, Logos and other persuasive strategies like use of personal pronoun, predication strategy, and positive self-presentation and negative others-presentation employed by IK, and further to study how language carries the power of transforming the perception and political views of people. The findings suggest that political discourse is intentionally crafted to communicate and persuade people about specific ideologies located in the discourse in an implicit way and IK uses the Aristotelian rhetorical model comprising of rhetoric, predication strategy, and self-presentation and negative Others-presentation strategy to persuade his audience to follow his hidden agendas.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".