Competing Motivations (the Need to Help and Need to Conserve) Bias Attitudes and Helping Behavior Toward Natural Disaster Victims
Bibliographic record
Abstract
In this investigation, Weiner’s (1995) attribution theory is integrated with Hobfoll’s conservation of resources theory to build upon previous research explaining donor support variations in natural disasters (Marjanovic et al., 2008). Utilizing three experiments conducted within three months of real disasters (Hurricane Dorian, Australian Bushfires, Typhoon Rai) and diverse Canadian samples from Amazon’s Mechanical Turk, the study manipulated victim preparedness and plea timing to examine their biasing effects on attribution processes and helping behavior. Hypothesis 1 asserts that victims who are portrayed as having done everything they could to minimize or avoid a foreseeable disaster before it occurred will evoke participants’ need to help. As reflected through the stages of Weiner’s model, these victims will be judged not responsible for their predicament, be afforded sympathy and a willingness to help, and elicit helping behavior. In contrast, victims portrayed as unprepared for a foreseeable disaster will be judged responsible and blamed for the event. They will elicit anger, little willingness to help, and generate low levels of helping behavior. Hypothesis 2 asserts that Low-NCR (i.e., Late Plea Timing) participants will judge victims less harshly than High-NCR (i.e., Ealy Plea Timing). To a lesser extent but still significant, Low-NCR participant attitudes will be less angry, more sympathetic, and more willing to help victims. Lastly, Low-NCR participants engage in greater Helping Behavior than High-NCR participants. Hypothesis 3 posits that in the Early Plea condition, participants will be inclined to conserve resources and motivated not to allocate their participation payment generously. Consequently, they may denigrate victims in the Responsible condition and offer minimal assistance. In contrast, as they find no faults with the Not Responsible victims, they are likely to sympathize with them, express willingness to help, and donate a larger portion of their participation payments to aid in their recovery. Thus, in the Early Plea condition, distinctions in attitudes and behavior toward victims between the Responsible and Not Responsible groups are expected to be perceptible and pronounced. Conversely, in the Late Plea condition, differences between Responsible and Not Responsible outcomes are predicted to be minimal. Participants are expected to have formed attitudes before scrutinizing accountability, resulting in less sensible attitudes and prosocial behavior towards victims, with a more equitable distribution of help to both Responsible and Not Responsible victims, and less pronounced distinctions between the two.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.000 |
| Open science | 0.001 | 0.002 |
| 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".