Motivated to Forgive? Partisan Scandals and Party Supporters
Bibliographic record
Abstract
In this study, we investigate how partisan motivations shape voters' reactions to a political scandal by drawing on a unique survey experiment fielded immediately after Justin Trudeau's brownface/blackface scandal broke during the 2019 Canadian election. We thus explore motivated reasoning in real time in a competitive and highly partisan election context. Are voters more willing to forgive politicians for past behavior when their own party leader's impropriety is cued? To what extent do personal interests, such as cross‐pressures or electoral concerns, affect the motivation to forgive? Our findings show that partisan‐motivated reasoning is overwhelmingly powerful, producing politically biased judgments of politicians implicated in scandals. Furthermore, voters' willingness to forgive scandals is also influenced by “strategic” considerations, in that preferences over which political party wins or loses in the election affect opinions about whether someone should be forgiven or whether the scandal is considered important at all. However, we find no evidence that personal involvement in the issue raised by the scandal conditions partisan motivations. We posit that the environment—in this case, a competitive election—is an important consideration for understanding the extent and limits of partisan‐motivated reasoning.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".