The Minds of Humans Are Mirrors to One Another: Essays on the Place of Empathy in Individual Decision-Making and Opinion Formation
Bibliographic record
Abstract
Empathy has long been regarded as a virtue. Early philosophers argued that it is central for cultivating moral judgment and fostering social harmony. Over the past 50 years, psychologists, biologists, and primatologists have reinforced this view, suggesting that empathy played a central role in human evolution by promoting prosocial behaviour and enabling cohesive societies. However, recent scholarship has challenged this optimistic perspective, showing that empathy, like many cognitive processes, is subject to bias. It can foster altruism and cooperation, but it can also be selective and shaped by social boundaries, sometimes leading to exclusion and polarization. This dissertation argues that to fully understand empathy’s political effects, we must recognize this duality. It explores \textit{how}, \textit{why}, and \textit{when} empathy shapes political attitudes and behaviours. The first article asks how empathy can produce both positive and negative outcomes. It distinguishes between cognitive (perspective-taking) and affective (empathic concern) components, showing with U.S. and Canadian data that perspective-taking reduces animosity toward political out-groups, while empathic concern may reinforce in-group favouritism and deepen partisan divides. The second article explores why empathy’s biases influence political behaviour. Drawing on intergroup bias theory, it investigates whether individuals are more inclined to empathize with their own party than with political opponents. Although I expected partisans to avoid empathizing with out-group members due to cognitive effort and identity threats, a survey experiment reveals they are not less motivated to do so. Still, those more willing to engage in affective empathy show higher levels of affective polarization, suggesting empathy can intensify political divides under certain conditions. The third article focuses on when empathy can be mobilized to foster political engagement, especially on climate change. A large-scale U.S. experiment tested the effects of flood imagery, real and AI-generated, on empathy and climate attitudes. Both types elicited empathy, which in turn influenced concern and behavioural intentions. These findings show empathy can be a catalyst for action when effectively activated. Overall, this dissertation demonstrates that empathy is a malleable and motivated process whose political effects depend on how, why, and when it is activated. It can both bridge and deepen political divides.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.013 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".