An Analysis on Stylistic Features of Donald Trump’s Speech
Why this work is in the frame
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Bibliographic record
Abstract
For government or leaders, public speaking is an important way to show the statesmanship and eloquence. It is a means of attracting groups of people who come from different classes. As the president of the United States, Donald Trump’s speaking talent plays an important role in the general election. Stylistics, which uses theories of modern linguistics to solve problems, aims at studying linguistic features and revealing the effect and function of pragmatic expression. This article selected Donald Trump’s three typical speeches, which studies from the perspective of stylistics on three major aspects—language description, textual analysis and contextual analysis. The analysis yielded the following results, 1) Language description consists of lexical analysis and syntactic analysis. On lexical level, Trump tends to use more abstract nouns and first person plural pronoun to make the addresses persuasive and more acceptable. Syntactically, for the sake of expressing information effectively and attracting more support, simple sentences and declarative sentences are prevailing in the speeches; 2) On the aspect of textual analysis, Trump employs topical division, problem-solution division and chronological division in an overlapping way in main body of speeches and creates crescendo in closure; 3) Contextual analysis shows that language varies from situations and they are formal and highly-structured. In a word, to analyze Donald Trump’s speech on stylistic features is significant for us on observing the features of his speeches and word-using habits.
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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 | 0.001 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it