Norms of Fairness and Generosity Among People Experiencing Homelessness: A Dictator Game Field Experiment
Bibliographic record
Abstract
Society often ascribes negative stereotypes to people experiencing homelessness. However, people experiencing homelessness have been found to display highly nuanced social behaviors. We employ a field dictator game to examine prosocial behavior among 173 unhoused individuals in Nashville, TN. We test whether an unhoused population displays ingroup bias, wherein they are more generous toward other people experiencing homelessness (the hypothesized ingroup) than people not experiencing homelessness (the hypothesized out-group). Additionally, we explore relationships between sociodemographic and personal characteristics (social support, perceptions of deservedness/generosity) and dictator game behavior. We did not observe ingroup bias. However, on average, participants allocated 29% of their game endowment to recipients, consistent with cross-cultural dictator game studies. We found that the duration of homelessness, social support, and gender were associated with dictator game allocations. Additionally, people experiencing homelessness were more generous when they perceived other unhoused individuals would be more generous and deserving.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".