Bibliographic record
Abstract
Empathy has many benefits. When we are willing to empathize, we are more likely to act prosocially (and receive help from others in the future), to have satisfying relationships, and to be viewed as moral actors. Moreover, empathizing in certain contexts can actually feel good, regardless of the content of the emotion itself—for example, we might feel a sense of connection after empathizing with and supporting a grieving friend. Does this feeling come from empathy itself, or from its real and implied consequences? We suggest that the rewards that flow from empathy confound our experience of it, and that the pleasant feelings associated with engaging empathy are extrinsically tied to the results of some action, not to the experience of empathy itself. When we observe people’s decisions related to empathy in the absence of these acquired rewards, as we can in experimental settings, empathy appears decidedly less pleasant. Empathy has many benefits. When we are willing to empathize, we are more likely to act prosocially (and receive help from others in the future), to have satisfying relationships, and to be viewed as moral actors. Moreover, empathizing in certain contexts can actually feel good, regardless of the content of the emotion itself—for example, we might feel a sense of connection after empathizing with and supporting a grieving friend. Does this feeling come from empathy itself, or from its real and implied consequences? We suggest that the rewards that flow from empathy confound our experience of it, and that the pleasant feelings associated with engaging empathy are extrinsically tied to the results of some action, not to the experience of empathy itself. When we observe people’s decisions related to empathy in the absence of these acquired rewards, as we can in experimental settings, empathy appears decidedly less pleasant.
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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 | 0.005 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.013 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".