Social psychological research on prejudice as collective action supporting emergent ingroup members
Bibliographic record
Abstract
Why does social psychological research on prejudice change across time? We argue that scientific change is not simply a result of empirical evidence, technological developments, or social controversies, but rather emerges out of social change-driven shifts in how researchers categorize themselves and others within their larger societies. As mainstream researchers increasingly recategorize former outgroup members as part of a novel ingroup, prejudice research shifts in support of emergent ingroup members against their emergent outgroup opponents. Although social change-driven science results in valuable opportunities for researchers, it also results in significant risks for research - collective, scientific biases in the inclusion and exclusion of social groups in prejudice research that are not readily detected or managed by traditional controls. We present the Emergent Ingroup Model (EIM) to encourage reflection on shared biases, as well as to spark a broader conversation on how to strengthen our field for a rapidly changing and increasingly global world.
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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 | 0.006 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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; 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".