Cognitive Process of Peer-Aggressive Scenes Between Adolescents with History of Peer-Aggression and Peer Victimization
Bibliographic record
Abstract
The purpose of this study is to examine group differences in eye movement between people with a history of perpetrating in peer aggression and people with a history of peer victimization on the processing of peer-aggressive and non-aggressive scenes. As Richard Hazler (1996) claimed that ‘bullies only see the event and its result from their own perspectives’, children who perpetrate in peer-aggression may attend to different social cues than those who do not engage in peer-aggression or who are victimized by peer-aggression. To better understand these differences, we need a direct assessment of their attention. Thus, in my study, the eye movements of participants are recorded while they are presented with aggressive and non aggressive scenes. As previous studies suggested that individuals with a history of perpetrating in aggression are more likely to pay attention to aggressive stimuli, I predict that the aggressors will pay more attention to the bullying targets than the victimized targets in aggressive scenes. I also expect that the aggressors would pay more attention to the bullying targets in aggressive scenes than the victims would. This study should expand our knowledge on cognitive processes of peer-aggressors and may inform the development of more effective bullying intervention programs where selective attention of peer-aggressors could be guided to reduce their biased perception of social situation.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".