The Language of Legacies: The Politics of Evoking Dead Leaders
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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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 | 0.005 | 0.027 |
| 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 | 0.007 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".