Technologization of Discourse: Technologization of American Foreign Policy Discourse in the Middle East in President Donald Trump’s Selected Speeches
Bibliographic record
Abstract
Discourse of the powerful is normally characterised as hegemonic since it lacks, whether consciously or unconsciously, familiarity of historical events and intrinsic knowledge of culture. President Trump’s disregard for or over-simplification of the complexities of Middle Eastern cultures in particular, their history, politics, and political geography, his belligerent and rapacious entrepreneurial rhetoric, his authoritarian stance on many global issues combined with the superficial allure of his ‘common man’ persona, and his hegemonising of many of the world’s nations make him the antithesis of the conventional politician. The process of technologization of Donald Trump’s discourse that has been influencing the discursive practices surrounding Middle East politics and American foreign policy has given rise to a type of discourse that is unprecedented in modern time American presidencies, a discourse rooted in threat, disregard, and humiliation. Trump’s disrespect to heads of states entails disrespect of their people, history, and culture, and this is prevalent in almost all his speeches. This paper focuses primarily on Trump’s rhetorical styles in his 28th September 2018 address to the UN, the May 14th 2018 address on the U.S. Embassy move from Tel Aviv to Jerusalem, and finally the April 13th 2018 address on the Syrian airstrikes.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.015 | 0.020 |
| Scholarly communication | 0.014 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".