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Record W6962533443 · doi:10.17605/osf.io/4a6c7

Competing Motivations (the Need to Help and Need to Conserve) Bias Attitudes and Helping Behavior Toward Natural Disaster Victims

2024· other· en· W6962533443 on OpenAlexaboutno aff

Bibliographic record

VenueOpen Science Framework · 2024
Typeother
Languageen
FieldSocial Sciences
TopicRisk Perception and Management
Canadian institutionsnot available
Fundersnot available
KeywordsPleaAttributionHelping behaviorSympathyVignetteNatural disasterBlameProsocial behaviorAgency (philosophy)Payment

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.002
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.069
GPT teacher head0.398
Teacher spread0.329 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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