Attending to Bullying: A Psychophysical Approach to Understanding Peer Aggression and Victimization
Bibliographic record
Abstract
Intervention and prevention programs for peer aggression in schools have largely been constructed with the assumption that aggressive children will pay more attention to aggressive stimuli in their social environment. However, this hypothesis has never been tested with direct measures of attention. Thus, my honours thesis project is investigating how participants with a history of peer aggression involvement as a perpetrator or victim direct their attention in photographs depicting scenarios of peer aggression. Based on answers in a questionnaire, participants were divided into three groups: (1) those with a history of perpetrating peer aggression, (2) those with a history of being victimized by peer aggression and (3) those with no history of peer aggression involvement. The experimental study was conducted on an eyetracker, which measured where participants were looking as they viewed 48 photographs. The photographs depicted preadolescent children in scenes of group or peer‐to‐peer interactions that were either aggressive or non‐aggressive. I predict that those with a history of aggression will pay more attention to the aggressors in the scenes more often than those with a history of victimization or the control group. This investigation will give direct evidence regarding how attention in social scenes is affected by a history of peer aggression involvement. A better understanding of how attentional processes are affected by a history of peer aggression involvement will allow for the development of more effective intervention and prevention programs for peer aggression in schools.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".