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Record W4412637572 · doi:10.1002/mar.70012

Contagion of Inequality: How Perceiving Income Inequality Deters Animal Welfare Consumption

2025· article· en· W4412637572 on OpenAlexaff
Danny JM Kim, Sunyee Yoon, Jeffrey P. Boichuk

Bibliographic record

VenuePsychology and Marketing · 2025
Typearticle
Languageen
FieldNeuroscience
TopicPsychology of Moral and Emotional Judgment
Canadian institutionsWestern University
Fundersnot available
KeywordsInequalityConsumption (sociology)EconomicsEconomic inequalityWelfareSocial inequalityIncome inequality metricsDemographic economicsLabour economicsPublic economicsSociologyMarket economyMathematics

Abstract

fetched live from OpenAlex

ABSTRACT Despite the increasing availability of animal welfare products (i.e., consumer goods produced with consideration for animal welfare in the supply chain), conventional products still dominate the market. Drawing from system justification theory, which suggests that consumers tend to justify the shortcomings of their society, we show that perceiving income inequality is one of the factors that deter consumers from switching to animal welfare products. Our research reveals consumers' motivation to justify income inequality leads them to accept the idea of more competent groups dominating less competent groups (i.e., social dominance orientation), which, in turn, legitimizes humans' dominion over nonhuman animals. Five experimental studies demonstrate that consumers who encounter high (vs. low) income inequality are less inclined to prefer animal welfare products, and this effect is attributed to an enhanced social dominance orientation. We outline the situations in which this effect is mitigated (i.e., when a meritocratic belief is invalid, and when animals are not viewed as inferior to humans) and discuss how animal welfare products can be promoted amid growing income inequality worldwide.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.463
Threshold uncertainty score0.537

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.342
Teacher spread0.255 · 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.

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
Published2025
Admission routes1
Has abstractyes

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