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.
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 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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".