Impact of Use of Language on Audience’s Perception: A Qualitative Analysis of Speeches by Hillary Clinton
Bibliographic record
Abstract
Discourse is a fundamental factor to communicate and to identify a language or use of language. Therefore, the language used in political discourse is important for the candidates to persuade the voters. In the light of Hillary Clinton’s political discourses, interviews and debates, the present study aims to identify the impact of her language on audience’s perception. A total of Clinton’s 29 debates and 3 interviews have been extracted from YouTube, which were transcribed in the written text. The findings of the study revealed that pronouns, metaphors, and rhetoric aspects in the speeches fulfil the strategic communicative functions. This allows the study to present a political agenda, identify the important issues, and highlight her political actions. The media portrayal of Clinton’s leadership skills and language used in political speeches had a great impact on the voters. Therefore, more research should be conducted to recognize what voters want from a female candidate as a president.
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How this classification was reachedexpand
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 | 0.000 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".