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Record W2311464513 · doi:10.1093/jcr/ucv096

Wealth and Welfare: Divergent Moral Reactions to Ethical Consumer Choices

2016· article· en· W2311464513 on OpenAlexaff
Jenny G. Olson, Brent McFerran, Andrea C. Morales, Darren W. Dahl

Bibliographic record

VenueJournal of Consumer Research · 2016
Typearticle
Languageen
FieldDecision Sciences
TopicEthics in Business and Education
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsAttributionScrutinyPerceptionGovernment (linguistics)Social psychologyMoral responsibilityWelfareTaxpayerPublic economicsBusiness ethicsMoral disengagementPsychologyEconomicsPolitical sciencePublic relationsLaw

Abstract

fetched live from OpenAlex

Abstract This article examines perceptions of low-income consumers receiving government assistance and the choices they make, showing that this group is viewed differently than those with more resources, even when making identical choices. A series of five experiments reveal that ethical purchases polarize moral judgments: whereas individuals receiving government assistance are perceived as less moral when choosing ethical (vs. conventional) products, income earners, particularly high-income individuals, are perceived as more moral for making the identical choice. Price is a central component of this effect because equating the cost of ethical and conventional goods provides those receiving government assistance some protection against harsh moral judgments when choosing ethically. Moreover, earning one’s income drives perceptions of deservingness, or the right to spend as one desires. Those who receive assistance via taxpayer dollars are under greater scrutiny (frequently resulting in harsher moral judgments) by others. In addition to influencing perceptions of individual consumers, the results demonstrate that such attributions extend to groups who make ethical choices on others’ behalf, and that these attributions have real monetary consequences for nonprofit organizations.

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)0.020
metaresearch head score (Gemma)0.030
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.565
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0200.030
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.478
GPT teacher head0.552
Teacher spread0.074 · 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 teacher head, not a consensus.

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

Citations120
Published2016
Admission routes1
Has abstractyes

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