Differentiating the sources of post‐election partisan affect warming
Bibliographic record
Abstract
Abstract While scholars have closely examined the intensification of negative affect across party lines during elections, less is known about the decline of partisan hostility in the aftermath of election campaigns. Synthesizing insights from research on electoral rules and political psychology, we theorize and empirically test two such mechanisms of post‐election negative affect decline. The first is that of winners' generosity: the expectation that self‐perceived election winners will express warmer feelings towards political opponents. The second is that of co‐governance, which predicts that shared coalition status leads to warmer affective evaluations among governing parties. We provide evidence that these mechanisms operate as pressure valves of negative partisan affect. We also show that while co‐governance reduces negative affect between parties who govern together, it fuels negative affect among supporters of opposition parties. The empirical analyses leverage a uniquely uncertain political period following the 2021 Israeli elections, around which we conducted an original panel study. Our findings advance the comparative polarization literature and connect psychological and institutional accounts of temporal fluctuations in partisan affect.
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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 | 0.001 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".