Social Conflict, Harmony, and Integration
Bibliographic record
Abstract
Abstract This chapter examines psychological perspectives on intergroup relations, their implications for reducing bias and conflict, and their potential applications for enhancing social integration. Psychological research on social conflict, harmony, and integration has adopted one of two general perspectives. One perspective places an emphasis on functional relations between groups, typically pointing to competition and consequent perceived threat as a fundamental cause of intergroup prejudice and conflict (e.g., realistic group conflict theory). Another approach focuses on the roles of social categorization and collective identity, indicating that whereas different group identities tend to promote conflict, a common ingroup identity promotes harmony (e.g., social identity theory). Although functional and social categorization theories propose different psychological mechanisms, these approaches offer complementary rather than necessarily competing perspectives. For example, within the context of the Contact Hypothesis, appropriately structured intergroup contact can reduce bias and conflict by creating cooperative relations between groups while producing more individuated and personalized perceptions of others (decategorization) or creating a shared sense of identity (recategorization). Pragmatically, understanding the nature of bias and the processes that underlie it can suggest ways that these forces can be harnessed and redirected to promote social harmony.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".