Survival of the Fittest: Rhetoric During the Course of an Election Campaign
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Despite the tradition of studying campaign effects, we know little about the rhetorical strategies of candidates. This study speculates about the types of appeals that incumbents and challengers find most effective and that are, as a result, most likely to dominate an election campaign. Candidates have an incentive to use arguments that evoke emotions such as fear, anxiety, and anger. Emotional appeals allow candidates to emphasize consensual values, which makes it easier to mobilize their party's base while simultaneously attracting the support of the uncommitted. The use of emotional appeals is also consistent with the media's preference for drama and excitement in news reporting. Thus, emotional appeals will be more enduring than other types of appeals, and hence more likely to dominate the rhetorical landscape. A content analysis of newspaper coverage of the 1988 Canadian federal election campaign provides suggestive evidence in favor of this view.
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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 | 0.001 |
| 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 it