Contagion of Inequality: How Perceiving Income Inequality Deters Animal Welfare Consumption
Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".