Linguistic and Stylistic Features of English Public Speeches
Bibliographic record
Abstract
Political communication plays a special role in the life of modern society. Political speeches can be used to judge the direction of the development of political relations between states and the priorities of politicians in various spheres of social and political life. A political public speech is nothing but the interaction of a politician with the audience, a means of propaganda, a presentation of his position and views, a means of persuasion, and a tool for the power struggle. In political speeches, the important role of language as a means of struggle for power and a way to retain it is especially evident. This determines the relevance of studying the linguistic and stylistic features of political speech and identifying effective ways of linguistic influence on a wide audience. The scientific novelty of the article lies in the description of linguistic and stylistic means that contribute to the creation of an effective political speech in English on the example of the speeches of US President Donald Trump, whose speech style is of great interest to linguists. The article aims to describe the linguistic and stylistic means of creating expressiveness that Trump prefers in his speeches. An equally important objective is to determine the main functions of using linguistic and stylistic devices in a political speech.
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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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".