Selective exposure and the authoritarian dynamic: Evidence from Canada and the United States
Bibliographic record
Abstract
This study explores to what extent selective exposure to political messages can produce political (in)tolerance among authoritarians and non-authoritarians. Drawing on a selection-exposure experiment embedded within an online survey conducted in the United States (N = 1978) and Canada (N = 1673), we explore how authoritarians and non-authoritarians react to framing around civil liberties controversies. Participants were randomly assigned to receive a message about a controversial group. In the forced-choice condition, participants were randomly assigned a political or non-political message. In a second condition, participants were given a choice of which message to read more about. The results show that authoritarians who are politically knowledgeable generally avoid messages that promote free speech by consuming non-political information. While messages about the dangers of free speech have the potential to produce more intolerance among authoritarians, we found that this effect was limited to those who are the least likely to consume them when given a choice. By contrast, we found that messages about the risk posed by free speech produced intolerance among non-authoritarians for whom threat-related cognitions were already chronically accessible. The effects of pro-civil liberties messages were limited to unthreatened non-authoritarians. Hence, we conclude that in the contemporary information environment selective exposure can increase polarization around a civil liberties controversy by producing attitude change but this occurs mainly among non-authoritarians.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".