The Language of Legacies: The Politics of Evoking Dead Leaders
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
How can leaders recover public trust and approval when government performance is low? We argue politicians use speeches evoking images of deceased predecessors to reactivate support temporarily. This distracts supporters from the poor performance and arouses empathy and nostalgia among them, causing them to perceive the current leader more favorably. We test this argument by scraping for all speeches by Argentine president Cristina Fernández de Kirchner. We identify all instances when she referenced Juan Perón—the charismatic founder of the Justice Party. We find that as Kirchner’s approval rating decreases, the number of Perón references increases. To identify the causal mechanism and to ensure that endogeneity is not a concern, we employ text analysis and a natural experiment—courtesy of LAPOP. The results provide robust evidence that leaders reference their dead predecessors to evoke positive feelings. However, while doing so can improve public opinion, the effects manifest only in the short term and among supporters.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| 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