Disentangling functions of online aggression: The Cyber‐Aggression Typology Questionnaire (CATQ)
Bibliographic record
Abstract
Aggression in online contexts has received much attention over the last decade, yet there is a need for measures identifying the proximal psychological drivers of cyber-aggressive behavior. The purpose of this study was to present data on the newly developed Cyber-Aggression Typology Questionnaire (CATQ) designed to distinguish between four distinct types of cyber-aggression on dimensions of motivational valence and self-control. A sample 314 undergraduate students participated in the study. The results confirmed the predicted four-factor structure providing evidence for distinct and independent impulsive-aversive, controlled-aversive, impulsive-appetitive, and controlled-appetitive cyber-aggression types. Further analyses with the Berlin Cyberbullying Questionnaire, Reactive Proactive Aggression Questionnaire, and the Behavior Inhibition and Activation Systems Scale provide support for convergent and divergent validity. Understanding the motivations facilitating cyber-aggressive behavior could aid researchers in the development of new prevention and intervention strategies that focus on individual differences in maladaptive proximal drivers of aggression. Aggr. Behav. 43:74-84, 2017. © 2016 Wiley Periodicals, Inc.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".