Discursive Strategies in Imran Khan’s Address to the United Nations General Assembly: Ideological Square Model Perspective
Bibliographic record
Abstract
Critical discourse analysis unearths the ideologies, beliefs, and principles embedded in spoken and written discourse. Imran Khan’s addresses to national and international forums gained the attention of the discourse analysts. The current study explored the discursive strategies used by ex-prime minister of Pakistan in his address to the 74th session of the United Nations General Assembly (UNGA). The present study utilized a mixed-method research design by employing Van Dijk’s Ideological Square Model analytical framework. The data of the study and the transcribed text of the speech were gleaned through internet sources. The text of the speech was analyzed through the content analysis approach. Furthermore, the quantitative section looked at the frequency of the discusrive strategies like description of actor burden, and authority all presented through frequency tables and percentages. The findings revealed that actor description (42%), authority (27%), and burden (18%) were the most frequently employed strategies, indicating Khan’s deliberate use of persuasive devices to reinforce ideological positioning at both textual and socio-cognitive levels. These strategies helped enhance the persuasiveness of his speech.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".