Necropolitical institutions and state-sanctioned violence: Critical discourse analysis of institutional response to a professional boxer's death in Quebec
Bibliographic record
Abstract
This study examines how necropolitical institutions legitimize state-sanctioned violence in Quebec's professional boxing. Through the case of Jeanette Zacarías Zapata's death, we analyze how state institutions simultaneously regulate and promote boxing, normalizing structural violence within the sport spectacle. Using Fairclough's model of critical discourse analysis, we examine the coroner's report and regulatory documents to demonstrate how official institutional frameworks construct discourses of “safe violence” while facilitating unequal risk distribution toward precarious athletes and creating institutional conditions privileging spectacle over welfare. This study extends sociocultural analysis of injury and death in sports via a necropolitical lens, examining how the state normalizes violence through institutional power. In doing so, this study aims to contribute to the discussion in three primary ways. First, it seeks to apply Mbembe's necropolitics to the material practices of sports regulation, through its institutions. Second, it approaches the coroner's report not as a neutral medical–legal document, but as a discursive artifact that actively constructs a reality to displace blame. Third, it examines the report's reliance on the contested “second impact syndrome” (SIS) diagnosis, suggesting it functions as a culturally loaded etiology that helps exonerate local institutions from responsibility. Ultimately, this study illuminates how state-sanctioned violence is rationalized and managed within the supposedly protective frameworks of regulated sport. By creating a “spectacle of safety” that masks systemic failures, these institutions perpetuate a system where certain lives—particularly those of marginalized athletes—are rendered disposable for entertainment and profit.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.024 | 0.024 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| 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".