Review of Monahan’s Crisis Vision: Race and the Cultural Production of Surveillance
Bibliographic record
Abstract
It was a jarring coincidence that I first picked up Torin Monahan's Crisis Vision: Race and the Cultural Production of Surveillance (Duke University Press, 2022) on January 28 of this year, the same day that officials in Memphis, Tennessee, announced their decision to release video evidence of what they described as the "inhuman," "unlawful," and "unthinkable" deadly beating of twenty-eight-year-old Tyre Nichols at the hands of five police officers a few weeks prior.The killing of Nichols, the city's response, and the devastating video footage released the following day, drew renewed attention to the prevalence of police violence in the US and reenergized calls for a national reckoning regarding the ways in which Black bodies in particular are surveilled, hyper-policed, dehumanized, and all too often brutalized by state-sanctioned actors.These topics are central to Crisis Vision, which offers a compelling set of intellectual frameworks for thinking through the racial logics that sustain contemporary surveillance practices and the scopic regimes that enable race-based state violence.Centering critical art practices that work to unsettle such logics and regimes, Monahan recognizes in art and visual culture a unique capacity to "organize collective attention to cartographies of state and corporate [surveillance] and violence" (18).Crisis Vision makes the convincing case that certain aesthetic strategies, namely those that abjure a politics of visibility in favor of an ethics of collective opacity, are especially adept at disrupting the activation and weaponization of surveillance culture today.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".