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Record W4399712838 · doi:10.32920/26046664.v1

Brutal Aesthetics and the Visual Economy of Digital Black Death

2024· preprint· en· W4399712838 on OpenAlexaffabout
Nataleah Hunter-Young

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldSocial Sciences
TopicPublic Spaces through Art
Canadian institutionsToronto Metropolitan UniversityCentre for Social InnovationMcMaster UniversityYork University
Fundersnot available
KeywordsNaturalizationState (computer science)SociologyEveryday lifePoliticsSocial mediaVisual cultureVisual artsAestheticsArtMedia studiesPolitical scienceLawCitizenship

Abstract

fetched live from OpenAlex

<p>"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.</p>

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.841
Threshold uncertainty score0.663

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.022
GPT teacher head0.325
Teacher spread0.303 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2024
Admission routes2
Has abstractyes

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