Who Helps Natural‐Disaster Victims? Assessment of Trait and Situational Predictors
Bibliographic record
Abstract
This investigation examined whether trait variables (empathy, global social responsibility) and perceived human responsibility predict and interact to predict people's helping of natural‐disaster victims. In Study 1, participants completed a questionnaire and read one of two bogus earthquake reports which portrayed victims as either prepared or unprepared for a foreseeable earthquake. In Study 2, participants completed a questionnaire about the victims of Hurricane Katrina. Across studies, helping was best elicited from high‐empathy individuals who attributed responsibility for disasters to human actions (e.g., government), not natural phenomena (e.g., hurricane). Trait variables correlated with helping when assessed individually, but accounted for little unique variance in helping in multiple regression analyses. Judgment of human responsibility predicted helping when participants were familiar with the target disaster (Study 2) but did not predict helping when the disaster was unfamiliar (Study 1). Theoretical implications for researchers and practical implications for aid agencies are discussed.
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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 | 0.007 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".