Happy to help—if it’s not too sad: The effect of mood on helping identifiable and unidentifiable victims
Bibliographic record
Abstract
People's preference to help single victims about whom they have some information is known as the identifiable victim effect. Previous research suggests that this effect stems from an intensive emotional reaction toward specific victims. The findings of two studies consistently show that the identifiability effect is attenuated when the subject is in a positive mood. Study 1 (along with a pilot study) demonstrate causal relationships between mood and identifiability, while using different manipulations to induce moods. In both studies, donations to identified victims exceeded donations to unidentified people-in the Negative Mood manipulations-while participants in the Positive Mood conditions showed no such preference. In Study 2, individual differences in people's moods interacted with the recipient's identifiability in predicting donations, demonstrating that the identifiability effect is attenuated by a positive mood. In addition, emotional reactions toward the victims replicate the donation pattern, suggesting emotions as a possible explanation for the observed donation pattern.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".