The brief aggression questionnaire: psychometric and behavioral evidence for an efficient measure of trait aggression
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
A key problem facing aggression research is how to measure individual differences in aggression accurately and efficiently without sacrificing reliability or validity. Researchers are increasingly demanding brief measures of aggression for use in applied settings, field studies, pretest screening, longitudinal, and daily diary studies. The authors selected the three highest loading items from each of the Aggression Questionnaire's (Buss & Perry, 1992) four subscales--Physical Aggression, Verbal Aggression, anger, and hostility--and developed an efficient 12-item measure of aggression--the Brief Aggression Questionnaire (BAQ). Across five studies (N = 3,996), the BAQ showed theoretically consistent patterns of convergent and discriminant validity with other self-report measures, consistent four-factor structures using factor analyses, adequate recovery of information using item response theory methods, stable test-retest reliability, and convergent validity with behavioral measures of aggression. The authors discuss the reliability, validity, and efficiency of the BAQ, along with its many potential applications.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it