Identifying Elements of Gender References, Persuasive Techniques and Social Interaction Associated with Political Discourse: The Case of Hillary Clinton
Bibliographic record
Abstract
Discourse is an important tool discussing social relations in the discursive patterns. A well-designed discourse can easily dominate people and can construct their perceptions. Therefore, discourse is critical in the political world when one uses it to communicate ideas and visions to the people. Therefore, the present study aims to identify the elements of gender references, persuasive techniques, and social interactions associated with political discourse of Hillary Clinton. The study has used the framework of conversation analysis for studying a total of three interviews and five debates of Hillary Clinton. The interviews and recording were extracted from YouTube and then transcribed and interpreted by converting them into text. The findings have revealed a significant use of persuasive techniques and social interaction in Clinton’s political discourse. The results also imply that using affiliation strategy, candidates can manipulate people. The study concludes that this strategy is more effective in representing oneself as truthful as compared to conventional narratives.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.036 | 0.021 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.004 | 0.004 |
| 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".