Valuing Liberty or Equality? Empathetic Personality and Political Intolerance of Harmful Speech
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Political tolerance is a core democratic value, yet a long-standing research agenda suggests that citizens are unwilling to put this value into practice when confronted by groups that they dislike. One of the most disliked groups, especially in recent times, are those promoting racist ideologies. Racist speech poses a challenge to the ideal of political tolerance because it challenges another core tenet of democratic politics – the value of equality. How do citizens deal with threats to equality when making decisions about what speech they believe should be allowed in their communities? In this article, we contribute to the rich literature on political tolerance, but focus on empathy as a key, and understudied, personality trait that should be central to how – and when – citizens reject certain types of speech. Empathy as a cognitive trait relates to one’s capacity to accurately perceive the feeling state of another person. Some people are more prone to worry and care about the feelings of other people, and such empathetic people should be most likely to reject speech that causes harm. Using a comparative online survey in Canada (n = 1,555) and the United States ( n = 1627) conducted in 2017, we examine whether empathetic personalities - as measured by a modified version of the Toronto Empathy Scale - predict the tolerance of political activities by “least-liked” as well as prejudicially motivated groups. Using both a standard least-liked political tolerance battery, as well as a vignette experiment that manipulates group type, we test whether higher levels of trait empathy negatively correlate with tolerance of racist speech. Our findings show that empathy powerfully moderates the ways in which citizens react to different forms of objectionable speech.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.006 |
| 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 it