Representing ‘Us’ and ‘Them’: Building Blocks of Intergroup Cognition
Bibliographic record
Abstract
Three experiments explored whether group membership affects the acquisition of richer information about social groups. Employing a minimal-groups paradigm, 6- to 8-year-olds were randomly assigned to 1 of 2 novel social groups. Experiment 1 demonstrated that immediately following random assignment to a novel group, children were more likely to generalize negative behaviors to outgroup members and positive behaviors to ingroup members and to report a preference for ingroup members. Experiments 2 and 3 showed that this initial ingroup-favoring bias interacts with subsequent learning, thereby attenuating the effect of negative information about the ingroup and enhancing the effect of negative information about the outgroup. These effects were more powerful with respect to preferences than induction: After hearing that some ingroup members behaved badly, children predicted that ingroup members would behave more negatively than outgroup members, but they did not express preferences for the outgroup over the ingroup. Together these data shed light on the construction of social category knowledge as well as the processes underlying the absence of own-group positivity among children from lower-status social groups.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| 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 | 0.003 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| 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".