Regional Intergroup Bias
Bibliographic record
Abstract
Recent advances in large-scale data collection have created new opportunities for psychological scientists who study intergroup bias. By leveraging big data, researchers can aggregate individual measures of intergroup bias into regional estimates to predict outcomes of consequence. This small-but-growing area of study has already impacted the field with well-powered research identifying relationships between regional intergroup biases and societally-important, ecologically-valid outcomes. In this chapter, we summarize existing regional intergroup bias research and review relevant theoretical perspectives. Next, we present new and recent evidence that cannot be explained by existing theory, and offer a new perspective on regional intergroup bias that highlights aggregation as changing its’ qualitative nature relative to individual intergroup bias. We conclude with a discussion of some of the important challenges that regional intergroup bias research will need to address in moving forward, focusing on issues of prediction and causality; constructs, measures, and data sources; and levels of analysis.
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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 | 0.051 | 0.114 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".