Brutal Aesthetics and the Visual Economy of Digital Black Death
Bibliographic record
Abstract
"Brutal Aesthetics and the Visual Economy of Digital Black Death" considers the social and cultural impacts of social media videos documenting anti-Black police brutality through the discursive interpretations of three Black visuals artists in Canada, the U.S., and South Africa. The interviewed artists--Anique Jordan, Cameron Granger, and Sethembile Msezane--are positioned within the study as both creative practitioners and theorists of visual communication. Our discussions act as entry points to analyze how this violent imagery has come to be installed in the everyday, accelerating a globalizing naturalization of anti-Black state violence. Guided by the work of Caribbean theorist Sylvia Wynter, this dissertation considers what each artist's creative text does rather than what it can be interpreted to mean, a method for identifying how these artworks act visually on the audience-spectator already attuned to the mundane violence of white supremacy. This SSHRC CGS and Pierre Elliott Trudeau Foundation funded research project extends the work of critical aesthetic theory to read the ways the accelerated naturalization of anti-Black state violence via state-corporate digital media surveillance works on popular perception to make Black death make sense. In evaluation of the imagery's visual and political economies, this project identifies what we can learn by studying the aesthetics of everyday life and what artists can teach us about how to look differently.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.029 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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".