Does Aggressive Commentary by Streamers during Violent Video Game Affect State Aggression in Adolescents?
Bibliographic record
Abstract
In the past 10 years, live-streaming services have gained huge popularity. Streamers usually play videogames and complement their performance with commentary. We examine the role of this streamer commentary on state aggression in Czech adolescents who were randomly assigned into one of three experimental groups (i.e., aggressive commentary, non-aggressive commentary, no commentary). The findings suggest that a short-term streamer’s commentary have no effect on affective and cognitive state aggression. In addition, the experimental conditions did not moderate any effects of personal traits (i.e., aggression, empathy) and long-term environmental factors (i.e., exposure to violence, watching violent streams, playing violent videogames) on state aggression. We found that trait aggression, trait affective empathy, and long-term exposure to violence were positively associated with state aggression, whereas trait sympathy was negatively associated with state aggression. The findings enrich the research with evidence for the lack of influence for streamer commentary on viewer aggression.
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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 | 0.001 | 0.004 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".