The Subversion Dilemma: Why Voters Who Cherish Democracy Participate in Democratic Backsliding
Bibliographic record
Abstract
Around the world, would-be authoritarian leaders have convinced their supporters to vote away the democracies they claim to cherish. How is this possible? We argue that simply fearing that opposing partisans support democratic backsliding can lead individuals to support it themselves. Would-be authoritarians may then be able to start a self-reinforcing dynamic of democratic backsliding by fostering these fears, which then generate exaggerated fears on the other. Using observational and experimental studies (N=4,400), we present four findings consistent with this account: Republicans and Democrats (1) overestimate opposing partisan willingness to break democratic norms; (2) will support their party breaking democratic norms themselves to the extent that they overestimate willingness by the other side; (3) that experimentally correcting this overestimation reduces support for breaking norms, and (4) increases the likelihood of voting for candidates that uphold democratic norms. Our findings suggest that we can foster democratic stability even in a highly polarized society using interventions that simply correct misperceptions about opposing partisans’ commitment to democratic norms
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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 | 0.006 | 0.034 |
| 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 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".