The partisan nature of support for democratic backsliding: A comparative perspective
Bibliographic record
Abstract
Abstract This article asks whether the willingness of partisans to condone democratic backsliding is a uniquely American phenomenon and explores why partisans would tolerate a party leader subverting democratic norms. We focus on executive aggrandizement as a key mechanism through which democratic backsliding occurs and develop three potential explanations for why partisans would accept the weakening of checks on the power of the executive. First, in a context of affective polarization, partisans may condone executive aggrandizement in order to advantage their party and disadvantage the opponent. Second, partisans may be willing to trade off democratic norms in pursuit of their ideological agenda. Third, partisans may take cues from the behaviour of party elites. These explanations are tested using a candidate‐choice conjoint experiment administered to Americans and Canadians in 2019 that involved respondents choosing between hypothetical candidates in intra‐party contests. Regardless of party, partisans in both countries proved willing to choose candidates who would loosen legislative and judicial restraints on the executive. While the partisan advantage explanation only held for strong Republicans in the United States, partisans in Canada and the United States alike were apparently willing to weaken restraints on the executive for the sake of their ideological agendas, at least in the case of abortion. Finally, Republicans who approved of the Trump presidency were much less likely than other Republicans to punish undemocratic candidates, lending support to the cue‐taking explanation.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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".