When the Majority Becomes the Minority: A Longitudinal Study of the Effects of Immersive Experience With Racial Out-Group Members on Implicit and Explicit Racial Biases
Bibliographic record
Abstract
The present study investigated the effects of immersive exposure to other-race individuals on racial bias. In Study 1, we tracked African students ( N = 85) who went to study at a Chinese university and thus experienced daily contact with Chinese individuals en masse for the first time. Using a cohort-sequential longitudinal design, we found that an implicit pro-Chinese racial bias emerged within 3 months after these students arrived in China, and that this bias remained stable for at least a year. In contrast, their explicit racial bias did not change. In Study 2, we assessed another group of African students ( N = 47) at 1 month and at 3 months after their arrival in China, looking at not only their implicit and explicit racial bias, but also their intergroup contact quantity, intergroup contact quality, and intergroup friendship. We found that intergroup contact quantity and intergroup friendship predicted implicit but not explicit racial bias 2 months later. The findings suggest that immersive experiences with racial out-groups can have early and lasting effects on implicit racial bias.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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; both teacher heads agree on what is shown here.
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".