The Empathy‐Prospect Model and the Choice to Help<sup>1</sup>
Bibliographic record
Abstract
This paper presents a model of the cognitive processes that precede decisions to help another person. The empathy‐prospect model predicts that potential helpers make decisions in much the same way as decision makers in other contexts do (i.e., they evaluate prospects) and that perceptions of need and the empathic reactions and intentions to help that they generate will be stronger for people observing losses rather than gains. The model also predicts that intentions to help should increase when (a) the predicament is serious, (b) money is not involved, or (c) help entails few costs for the potential altruist. The results from 2 experiments provide clear support for these predictions. The findings suggest that (a) the gains or losses of another person contribute to perceptions of that person's needs and feelings of empathy, (b) empathy is the primary proximal determinant of prosocial motivations, and (c) potential losses that are serious accentuate altruistic reactions.
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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 | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".