Know Me, Love Me, Fear Me: The Anatomy of Candidate Poster Designs in the 2007 French Legislative Elections
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
Candidates in many elections spend a significant amount of their budget on posters, yet we know virtually nothing about their communication roles. Based on party strategy and visual communication research, this article argues that poster content is the result of strategic choices by candidates, with major and niche candidates using significantly different poster designs in an effort to influence voters' evaluations. Using an original database of 256 candidate posters from the 2007 French legislative elections and content analysis computer software, I show that niche party candidates consistently emphasize partisan and factual information cues (through size and placement on posters), while major party candidates rely heavily on candidate-oriented visuals and on nonverbal cues (e.g., eye contact) to persuade voters. Preliminary analyses indicate that poster visual design strategies are significantly associated with both major and niche party candidates' electoral performance.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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