Does Talking to the Other Side Reduce Inter-Party Hostility? Evidence From Three Studies
Bibliographic record
Abstract
According to recent scholarship, citizens in various Western democracies show a growing sense of dislike and distrust toward members of opposing political parties. While political communication processes have been shown to influence inter-party hostility, the literature has so far focused mainly on mass-mediated communication. We argue here that affective polarization might also be determined by interpersonal political communication. Specifically, we hypothesize that “heterogeneous” political discussions—those transcending partisan and ideological boundaries—are associated with decreased hostility toward the other side. We test this hypothesis with three studies conducted in Canada: A cross-sectional survey (N = 3,596), a two- wave panel (N = 3,408), and an instrumental variable analysis (N = 2,005). We find that heterogeneous discussion indeed is associated with reduced polarization, a conclusion that holds across indicators of affect, obtains for both face-to-face and online discussions, and is consistent across studies. Having a heterogeneous (compared to homogeneous) discussion network predicts substantial decreases of up to 0.76, and no less than 0.09, standard deviations in out-party hostility. These findings inform scholarly debates about the antecedents of affective polarization and are consistent with the claim that cross-cutting political discussion can benefit democracy.
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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 | 0.007 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".