Response to 'Weathering anti-Blackness: injury, brain trauma, and neurodegeneration in American sport' by Tracie Canada and Chelsey R. Carter
Bibliographic record
Abstract
This is a response to 'Weathering anti-Blackness: injury, brain trauma, and neurodegeneration in American sport' by Tracie Canada and Chelsey R. Carter. Original article abstract: On June 2, 2021, the National Football League announced it would discontinue the use of race-norming in legal settlements for concussion-related injuries. The anti-Black practice of scientific racism at the foundation of race-norming disproportionately affected retired Black players’ ability to access compensation for medical issues they developed from sustained football play. On the same day, Major League Baseball celebrated its inaugural Lou Gehrig Day to honor the Yankees baseball player’s life and legacy, as he succumbed to amyotrophic lateral sclerosis (ALS) on this day decades before. This terminal neurodegenerative disease has been coded a “white disease” with Gehrig as its moniker, thereby rendering Black people invisible in ALS care and scientific spaces. Through events that occurred in these professional leagues on the same June day, we ethnographically theorize “weathering in sport” to discuss how Black athletes are both harmed and omitted from larger conversations about traumatic sport injuries and illnesses. We use the frames of weathering and the weather of anti-Blackness to emphasize the disproportionate impact of injury on Black athletic bodies from an anthropological perspective.
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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 | 0.007 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.007 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.015 | 0.039 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".