Bibliographic record
Abstract
Experimental research has shown that playing violent video games produces higher levels of aggressive cognition, aggressive affect, physiological arousal, and aggressive behavior (in the short-term) than non-violent video games (see Anderson, Gentile & Buckley, 2007). However, there are two major limitations with these studies. First, the majority of experimental studies that have compared the effects of violent versus non-violent video games on aggression have failed to equate these games in terms of competitiveness, difficulty, and pace of action. Thus, although the common finding is that violent video games produce higher levels of aggression than nonviolent video games, other unmatched factors beyond the actual violent content may be responsible for the elevated levels of aggression. Second, previous experimental studies have tended to use a measure of aggression that may also measure competitiveness, leading to questions about whether violent video games are related to aggression or competitiveness. The present thesis addressed these two issues by fIrst equating a violent and non-violent video game on competitiveness, difficulty and pace of action in Experiment I , and then comparing the effect of each game on aggressive behavior using an unambiguous measure of aggressive behavior (i.e., the Hot Sauce Paradigm). We found that video game violence was not sufficient to elevate aggressive behavior compared to a non-violent video game. Practical implications and directions for future research are discussed.
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 | 0.001 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".