Not so sexy: public opinion of political sex scandals as reflected in political cartoons
Bibliographic record
Abstract
An analysis of political cartoons of three well‐known US political sex scandals is presented to examine how cartoons channel public perception in terms of the involvement of a prominent politician and the difference of opinion based on who the politician is, frame the organization of communal knowledge, and facilitate the grounds upon which some things can be said. On one side of the spectrum, theories of political cartoons presume that political cartoons reflect public attitudes about current events and that by studying cartoons surrounding a sex scandal, public attitudes toward such a scandal can be effectively understood. On the other side of the spectrum, theory argues that cartoons actually persuade and shape public attitudes, intentions, and behaviors. Three prominent former US politicians were selected for the analysis: Eliot Spitzer and the Emperor's Club Bill Clinton and Monica Lewinsky John Edwards and Rielle Hunter The criteria of narrative, location, binary struggle, and normative transfer were used as a framework to analyze 230 cartoons. The findings suggest that some politicians emerge relatively unscathed from scandals, others are seriously condemned, yet others are obscured or have their roles changed substantially. On the basis of the research criteria, it seems that public reactions and sentiments toward politicians' involvement in a scandal depend on what the scandal was about, where it occurred and what happened there, who the protagonists in the conflict were, and who the loser in the story was. Copyright © 2011 John Wiley & Sons, Ltd.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".