Criminal anthroposcenes 2.0: Race, racism, and breath-taking violence in the time of COVID
Bibliographic record
Abstract
While media attention has focused on the visceral brutality of police chokeholds, less noticed are the breath-taking effects of air pollution caused by the (in)actions of state agencies dedicated to environmental protection. To think through how race and racism are embedded in the processes that underlie the Anthropocene, I reframe three key terms of engagement to analyze with greater rigor contemporary criminal anthroposcenes (i.e. scenes constituted by the inextricable enmeshing of crime and anthropogenic climate change): (1) climate and weather, (2) bodies and environments, and (3) anestheticization. Shaping a racial geography of dirty air, a climate of anti-Blackness in the US has been quietly impacting the health and lives of African Americans for centuries, so that the deadly impact of viral outbreaks can merge with existing modes of spectacular and slow violence. From the murder of George Floyd to the establishment of sacrifice zones, the complexity and messiness of recent breath-taking scenes of injustice are formed and maintained by a dangerous mixture of racial apathy and racially-charged violence.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".