Social class, power, and selfishness: When and why upper and lower class individuals behave unethically.
Bibliographic record
Abstract
Are the rich more unethical than the poor? To answer this question, the current research introduces a key conceptual distinction between selfish and unethical behavior. Based on this distinction, the current article offers 2 novel findings that illuminate the relationship between social class and unethical behavior. First, the effects of social class on unethical behavior are not invariant; rather, the effects of social class are moderated by whether unethical behavior benefits the self or others. Replicating past work, social class positively predicted unethical behavior; however, this relationship was only observed when that behavior was self-beneficial. When unethical behavior was performed to benefit others, social class negatively predicted unethical behavior; lower class individuals were more likely than upper class individuals to engage in unethical behavior. Overall, social class predicts people's tendency to behave selfishly, rather than predicting unethical behavior per se. Second, individuals' sense of power drove the effects of social class on unethical behavior. Evidence for this relationship was provided in three forms. First, income, but not education level, predicted unethical behavior. Second, feelings of power mediated the effect of social class on unethical behavior, but feelings of status did not. Third, two distinct manipulations of power produced the same moderation by self-versus-other beneficiary as was found with social class. The current theoretical framework and data both synthesize and help to explain a range of findings in the social class and power literatures.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".