Competition and intergroup bias: Toward a new construal process framework distinguishing competitive perceptions from competitive motivations
Bibliographic record
Abstract
Research on the effects of intergroup competition has relied on various conceptual approaches and has produced inconsistent findings. Following a review of the intergroup competition literature, we propose a framework which emphasizes that the influence of intergroup competition varies primarily according to participants’ construal of potentially competitive events. We assess this via two variables: competitive intergroup perceptions (CIP), the perception that one’s ingroup and another group(s) in the current situation are attempting to gain a reward at each other’s expense; and competitive intergroup motivations (CIM), the individual desire for one’s group to do better or acquire more of a reward than the other group(s). In four studies that presented participants with an ambiguously competitive intergroup setting and administered CIP and CIM scales adapted for the context, both variables were empirically nonredundant (Studies 1–4) and showed unique relations with intergroup outcomes: CIM, rather than CIP, was directly associated with greater intergroup bias (Studies 2–4), discriminatory intentions (Study 3), and discriminatory behavior (Study 4). CIP consistently registered an indirect effect on intergroup bias and behavior through CIM. Our results suggest that in ambiguous group contexts, CIM facilitates the pursuit of goals for the ingroup, which may involve expressing intergroup bias and more antisocial outgroup behavior, and that these tendencies are quite sensitive to corresponding changes in CIP. The current framework offers more precise insights into the influence of competitive group dynamics, and can easily be integrated with other research paradigms to determine how and when intergroup competition produces intergroup bias and discrimination.
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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 | 0.011 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.010 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".