Justice and Empathy: What Motivates People to Help Others?
Bibliographic record
Abstract
A core premise of the social psychology of justice is that people's attitudes, feelings, and behaviors are shaped by their subjective judgments about what is right or wrong, just or unjust, ethical or unethical (Tyler et al., 1997). This fundamental argument is now supported by a large and diverse body of literature. However, this was not always the case, and the work of Mel Lerner, both in his influential just world hypothesis (Lerner, 1980) and through several influential theoretical articles pointing out the broader implications of the justice motive (Lerner, 1981, 1982), has had a great deal to do with encouraging social psychologists to think seriously about the nature and importance of justice. Our goal in this chapter is to build upon Lerner's articulation of the existence of the justice motive, as well as upon the now large literature documenting the range and importance of the influence of people's concerns about justice. We do so by considering one core distinction within social psychology that we feel is underdeveloped at this time – the distinction between justice and empathy. Justice and empathy share (at least) one important behavioral influence. Both lead those who possess resources and/or power to help others who are needy or in distress. Understanding justice and empathy therefore also helps us to understand when and why people come to the aid of victims.
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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 | 0.001 | 0.001 |
| 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 | 0.006 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".