Does Aggressive Trait Induce Implicit Aggression among College Students? Priming Effect of Violent Stimuli and Aggressive Words
Bibliographic record
Abstract
The purpose of the study was to examine the priming effect of exposure to violent pictures on implicitaggression in a sample of 94 Chinese college students, and to verify the validity of General Aggression Model(GAM) and Cognitive New-association Model (CNM). Violent and nonviolent pictures, as well as aggressiveand nonaggressive words, were used as primes to explore the relationship between violent stimuli and implicitaggression of college students by employing modified Go-Nogo task. The results suggested that the primingeffect of exposure to violent pictures on participants was obvious, and that brief exposure to violent picturesincreased implicit aggression. Repeated measures analysis of variance (ANOVA) found that interaction betweenstimuli type (violent vs. nonviolent) and target word (aggressive vs. nonaggressive) was significant, implyingthat violent stimuli primed implicit aggression among college students. Further simple effect analysis showedthat implicit aggression was significantly primed by violent stimuli for participants with high aggressiveness(HA) and moderate aggressiveness (MA), but not for participants with low aggressiveness (LA). This resultshould be cautiously explained that only implicit aggression of college students with HA and MA wassignificantly primed by violent stimuli.
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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 | 0.000 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".