What explains elite affective polarization? Evidence from Canadian politicians
Bibliographic record
Abstract
Abstract Concerns about affective polarization are on the rise globally, and it has been associated with negative outcomes such as diminished trust in government and discrimination against out‐partisans. While elected politicians are typically thought to be a major source of mass‐level affective polarization, and despite the policy and representation consequences of heightened partisan hostility in legislatures, existing research has focused almost exclusively on the measurement and explanation of affective polarization among citizens. As a result, we know far less about the magnitude and sources of elite affective polarization. Here, we take a step towards addressing this gap using an original survey of hundreds of Canadian local politicians, a setting uniquely situated for addressing the role of a host of individual‐level and institutional‐level predictors of affective polarization. We find that Canadian local politicians are, on average, less affectively polarized than the citizens they represent. However, levels of affective polarization among these politicians vary considerably, with higher levels of affective polarization among politicians who are ideologues, partisans, and who harbor strong progressive ambition. We conclude by discussing the implications of our findings for research on affective polarization and describe the need for comparative studies of affective polarization among political elites.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".