Predicting Support for Social Actions and Policies that Enable Pet Ownership Among People Living in Poverty
Bibliographic record
Abstract
Despite numerous documented benefits derived from pets among those who are socially marginalized, public attitudes often reflect a perception that people living in poverty should not be pet owners. Two studies assessed individual differences that might predict reactions to those living in poverty as a function of the presence or absence of a pet, and support for social service policies that enable or restrict access to pets based on financial means. Both studies involved online surveys assessing individual differences (social justice orientation, individualistic beliefs about poverty, identifying/empathy with others, and attitudes toward animals), and novel outcome measures assessing responses to a person requesting financial assistance from passersby and to social service policies regarding pets and people living in poverty. In study 1 (n = 212), when the hypothetical person asking for financial assistance was accompanied by a pet, participants expressed less suspicion of them, and the presence of a pet caused those low in social justice orientation to express more concern and give more money. Social justice orientation and attitudes toward animals were associated with greater support for policies that enable pet care and diminished support for policies that restrict pet access. Study 2 (n = 278) largely replicated study 1. In addition, two conditions made differentially salient the benefits of the human–pet relationship for either pet or human wellness (vs. a control condition). The salience of benefits to the pet increased support for enabling policies among those with lower social justice orientations; making salient the human or pet benefits similarly affected support for such policies among those who were less empathetic toward animals. The findings suggest that contextual cues can raise awareness among some individuals who might not otherwise be compassionate toward people in poverty with pets.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".