Fans of Violent Music: The Role of Passion in Positive and Negative Emotional Experience
Why this work is in the frame
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Bibliographic record
Abstract
Extreme metal and rap music with violent themes are sometimes blamed for eliciting antisocial behaviours, but growing evidence suggests that music with violent themes can have positive emotional, cognitive, and social consequences for fans. We addressed this apparent paradox by comparing how fans of violent and non-violent music respond emotionally to music. We also characterised the psychosocial functions of music for fans of violent and non-violent music, and their passion for music. Fans of violent extreme metal ( n=46), violent rap ( n=49), and non-violent classical music ( n=50) responded to questionnaires evaluating the cognitive (self-reflection, self-regulation) and social (social bonding) functions of their preferred music and the nature of their passion for it. They then listened to four one-minute excerpts of music and rated ten emotional descriptors for each excerpt. The top five emotions reported by the three groups of fans were positive, with empowerment and joy the emotions rated highest. However, compared with classical music fans, fans of violent music assigned significantly lower ratings to positive emotions and higher ratings to negative emotions. Fans of violent music also utilised their preferred music for positive psychosocial functions to a similar or sometimes greater extent than classical fans. Harmonious passion for music predicted positive emotional outcomes for all three groups of fans, whereas obsessive passion predicted negative emotional outcomes. Those high in harmonious passion also tended to use music for cognitive and social functions. We propose that fans of violent music use their preferred music to induce an equal balance of positive and negative emotions.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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