Concussion killjoys: CTE, violence and the brain’s becoming
Bibliographic record
Abstract
Abstract CTE, or chronic traumatic encephalopathy, is caused by repetitive head trauma and detected by a distinctive stain for a protein called ‘tau’ in autopsied brain tissue. While the number of diagnosed patients is only in the hundreds, the cultural footprint of the disease in North America is huge, both because those diagnosed are often celebrity-athletes and because millions of children, adolescents and young men and women play collision sports like football and hockey. We argue that the widespread attention to CTE provides a useful wedge to crack open another, heretofore neglected public health concern: repetitive acts of violence in and around hypermasculine sports create subjects whose brains—and characters—are materially shaped by that violence. Brains change materially when delivering blows as well as receiving them, when participating in degrading hazing rituals as victim or assailant, when belittled or assaulted by a coach, when approaching an upcoming game riddled with fear. We adopt a biosocial model of the brain’s becoming to intervene in a linear discourse around CTE that medicalizes and oversimplifies violence, a story that prematurely dissects one slice of the problem from a noxious whole.
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.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.036 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".