A criminal mind? A damaged brain? Narratives of criminality and culpability in the celebrated case of Aaron Hernandez
Bibliographic record
Abstract
This article examines the media discourse surrounding the life and death of former National Football League player Aaron Hernandez, who died by suicide while incarcerated for first-degree murder. As a postmortem analysis found evidence of notable degenerative brain disease, differing explanations and speculations remain about the causes of his criminal behavior. This analysis illustrates how journalistic narratives attribute Hernandez’s criminality to either the material composition of his damaged brain or how his tumultuous background affected psychological makeup. Both narratives minimize the structural and political economic conditions that enabled this particular case of celebrated criminality. Cultural criminological and socio-legal insights aid in elucidating how notions of racialized masculinity and neurocriminology come to constitutively inform framings of Hernandez’s crimes, motivations, and actions while also directing critical attention away from the influence of relevant institutions, particularly sport, and instrumentalizing the role of violence. This article concludes with a reflection on the underpinning tensions revealed through depictions of Hernandez, his mind, and his brain, arguing that they surpass news and media stories and actually implicate debates about the growing influence of neuroscience in understandings of social problems, including crime.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.018 | 0.013 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.005 |
| 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".