Aggressive behavior and social status: An experimental test of the general aggression model
Bibliographic record
Abstract
In everyday situations, violations of social norms can be perceived as provocations that trigger aggression, which can have negative consequences for the individuals involved and social coexistence. Based on the General Aggression Model (GAM), we investigate how a physical provocation affects the internal state (cognition, arousal, and affect) of the provoked person and how this then affects their potential reactions. We also investigate the moderating effect of the provocateur's social status in this process. Using a scenario-based experiment within a representative sample of the working population in Germany ( N = 1,595), the level of provocation and the social status of the provocateur were experimentally manipulated, whereby three indicators of the internal state and three possible reactions (no reaction, verbal aggression, and physical aggression) were measured. Results show that an intentional provocation reduces the likelihood that the provocation will be ignored, while verbal or aggressive reactions become more likely. These effects were mediated by the provoked person's internal state. A higher social status of the provocateur resulted in a more aggressive-prone internal state. Moreover, the verbal reaction to provocation was significantly less affected by the internal state when the provocateur was of high social status. However, a simultaneous analysis of these processes shows that these countervailing conditioning effects of the social status offset the overall impact. This study offers insights into the dynamics of social interactions by demonstrating the functioning of a mechanism between provocation in everyday situations and the provoked individual's reactions, as well as the role of the provocateur's social status.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".