Social Categorization in Intergroup Contexts: Three Kinds of Self‐Categorization
Bibliographic record
Abstract
Abstract In reviewing self‐categorization theory and the literature upon which it is based, we conclude that individuals' attempts to form social categories could lead to three kinds of self‐categorization. We label them intergroup categorization, ingroup categorization, and outgroup categorization. We review literature supporting these three types and argue that they can help to explain and organize the existing evidence. Moreover, we conclude that distinguishing these three kinds of self‐categorization lead to novel predictions regarding social identity, social cognition, and groups. We offer some of those predictions by discussing their potential causes (building from optimal distinctiveness and security seeking literatures) and implications (on topics including prototype complexity, self‐stereotyping, stereotype formation, intergroup behavior, dual identity, conformity, and the psychological implications of perceiving uncategorized collections of people). This paper offers a platform from which to build theoretical and empirical advances in social identity, social cognition, and intergroup relations.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.010 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.003 |
| 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".