The Price of Dignity: Measuring Migrants' Metaperceptions using Behavioral Games
Bibliographic record
Abstract
How do migrants perceive host citizens’ stereotypes about them, and can these metaperceptions change behaviors? We theorize that migrants are cognizant of hosts' stereotypes against them, which drive them to make choices that seem irrational based on economic cost-benefit calculations but are rational to restore status and dignity. To test our argument, we conducted behavioral lab games in Colombia, with 600 citizens and Venezuelan migrants. By randomizing partners and varying the information on partners' nationalities, we identify bias for and against outgroups. We find across games that Venezuelans give more to Colombians when both players' nationalities are known, compared to the baseline of no information and when playing with other Venezuelans. These findings suggest that migrants may act against their own financial self-interest to counteract prevalent stereotypes, such as being freeloaders on state welfare. We also find qualitative evidence that migrants desire to regain dignity by countering hosts' negative stereotypes.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".