Owning Up to Negative Ingroup Traits: How Personal Autonomy Promotes the Integration of Group Identity
Bibliographic record
Abstract
OBJECTIVE: Our experiences, attributes, and behaviors are diverse, inconsistent, and often negative. Consequently, our capacity to assimilate divergent experiences-particularly negative aspects-is important to the development of a unified self. Whereas this process of integration has received attention at the level of personal identity, it has not been assessed at the level of group identity. OBJECTIVE: We examined the mechanisms involved in integrating positive and negative ingroup identities, as well as related outcomes. METHOD: In three experiments, participants (N = 332) high and low in autonomy identified either positive or negative aspects of their ingroup and then indicated the extent to which they integrated the attribute. RESULTS: Those high in personal autonomy integrated both positive and negative identities, whereas those low in autonomy acknowledged only positive identities. Study 2 showed that, regardless of identity valence, those high in autonomy felt satisfied and close with their group. Conversely, those low in autonomy felt less close and more dissatisfied with their group after reflecting on negative identities. Finally, reflecting on a negative identity reduced prejudice, but only for those high in autonomy. CONCLUSIONS: Owning up to negative group traits is facilitated by autonomy and demonstrates benefits for ingroup and intergroup processes.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".