Pumping Up the Base: Deployment of Strong Emotion and Simple Language in Presidential Nomination Acceptance Speeches
Bibliographic record
Abstract
Background and Method: This research examines the nomination acceptance speeches of US presidential candidates from Republican and Democratic parties in the post-WWII mass communication era (1948–2020, 38 speeches). Variables studied are the emotional tone of the speeches, their abstractness, their Grade Level, their employment of personal pronouns and their mentions of “America”. Speeches were scored with the Dictionary of Affect in Language (a sentiment analysis tool). Predictions: On the basis of functionalist theories of political discourse, it was predicted that the speeches would have a pleasant and active or celebratory emotional tone. Based on related research that focused on the effects of mass distribution on presidential communications, it was predicted that the speeches would increase in pleasantness, arousal and linguistic simplicity across years. Results: As predicted, speeches were pleasant and active in tone. Across years, speeches became significantly more arousing, less abstract, simpler, and longer. When individual speeches were divided into five equal portions, a strong significant quadratic trend was observed for pleasantness, which started high at the beginning of a speech, fell in the center, and rose again at the end. Conclusions: Presidential nomination acceptance speeches are emotionally pleasant and active and linguistically simple (Grade 8 level). Between 1948 and 2020, they remained pleasant, and became more active and simpler. In service of their aim to “pump up the base” individual speeches began on a pleasant, nationalistic and personal note, encompassed duller and more impersonal material in their centers, and became positive again at the end.
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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 | 0.000 |
| 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 | 0.000 | 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 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".