Negational Categorization and Intergroup Behavior
Bibliographic record
Abstract
Individuals define themselves, at times, as who they are (e.g., a psychologist) and, at other times, as who they are not (e.g., not an economist). Drawing on social identity, optimal distinctiveness, and balance theories, four studies examined the nature of negational identity relative to affirmational identity. One study explored the conditions that increase negational identification and found that activating the need for distinctiveness increased the accessibility of negational identities. Three additional studies revealed that negational categorization increased outgroup derogation relative to affirmational categorization and the authors argue that this effect is at least partially due to a focus on contrasting the self from the outgroup under negational categorization. Consistent with this argument, outgroup derogation following negational categorization was mitigated when connections to similar others were highlighted. By distinguishing negational identity from affirmational identity, a more complete picture of collective identity and intergroup behavior can start to emerge.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".