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Record W4407193291 · doi:10.1177/00222437251320021

Retributive Philanthropy

2025· article· en· W4407193291 on OpenAlexafffund
Ethan Milne, Kirk Kristofferson, Miranda Goode

Bibliographic record

VenueJournal of Marketing Research · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicNonprofit Sector and Volunteering
Canadian institutionsWestern University
FundersIvey Business School, Western UniversitySocial Sciences and Humanities Research Council of Canada
KeywordsRetributive justiceBusinessPsychologyEconomicsMicroeconomicsEconomic Justice

Abstract

fetched live from OpenAlex

Prosocial behavior research has historically considered altruistic or self-interested motives as the primary drivers for charitable giving. Recently, however, there have been many high-profile cases wherein consumers use their donations to harm others. The authors define this behavior, characterized by a desire for retribution resulting from witnessing or experiencing volitional wrongdoing, as "retributive philanthropy" and examine this phenomenon using a multimethod approach. Qualitative interviews with perpetrators and targets of retributive philanthropy reveal key themes of blameworthiness judgments, strong negative affect, and a desire to harm as a terminal goal of donation-none which are typically associated with prosocial behaviors. Analysis of real-world antivaccine protestor donation data finds similar themes of perceived wrongdoing and outrage related to retributive donations in a large-scale context. Five lab studies and five supplementary studies then demonstrate the effects of perceived volitional wrongdoing, harm, efficacy, and authoritarianism on willingness to make retributive donations. Together, these findings offer critical insight into an emerging mode of donation that is emotionally, motivationally, and behaviorally distinct from traditional prosocial behavior and has important implications for consumers and charitable marketers.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.048
metaresearch head score (Gemma)0.022
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.492
Threshold uncertainty score0.986

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0480.022
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.087
GPT teacher head0.515
Teacher spread0.428 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
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

Citations1
Published2025
Admission routes2
Has abstractyes

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