Comparative Study of Linguistic Features Used in the Inaugural Speeches of American Presidents
Bibliographic record
Abstract
Language is a very useful and powerful tool for communication and especially in political discourse it is very significant. During a political speech the leader tries to express, declare, commit, emphasize or motivate the listeners by using one’s ideology and power. Political personalities speak in different modes and tones and they announce their planning and aim to keep all aspects under their control and reduce the worry of the people. The aim of this paper is to identify the linguistic features used in the inaugural speeches of selected American Presidents and to analyze their functions using Critical Discourse Analysis theory proposed by Fairclough and the theory of Persuasion postulated by Aristotle. Researchers selected inaugural speeches of George W. Bush and Barack Obama. The study was an attempt to relate the inaugural discourse to the discursive social processes and to find covert ideology and power factors in the speeches. The findings revealed significant differences among the different linguistic features, discursive practices and rhetoric devices used in inaugural speeches of the two US presidents.
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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 | 0.002 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".