Group responses to deviance: Disentangling the motivational roles of collective enhancement and self-uncertainty reduction
Bibliographic record
Abstract
In this paper, we describe two basic motives for social identification: a drive for collective enhancement and a drive for epistemic fulfillment (uncertainty reduction). We posit that these two motives are critical for understanding one of the fundamental underlying mechanisms of social identity theory (SIT): positive distinctiveness, which is a desire to feel different from and better than relevant outgroups. Whereas “positive” was clearly outlined in the original social identity theory of intergroup relations, “distinctiveness” became a focal point of self-categorization theory. Most existing literature treats positive distinctiveness as a single construct; however, we argue that the “positive” and “distinctive” elements should be treated as separate but critically intertwined concepts. We suggest that “positive” is a direct feature of a desire for collective enhancement, and “distinctiveness” from a relevant outgroup is necessary for self-categorization that provides information to reduce self-uncertainty. Using the subjective group dynamics framework, which has historically emphasized the enhancement motive, we mathematically show that the motives act sequentially and differently to affect responses to deviance and change from it.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".