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Record W2982458085 · doi:10.1111/jaac.12680

Glued to the Image: A Critical Phenomenology of Racialization through Works of Art

2019· article· en· W2982458085 on OpenAlexafffundabout
Alia Al‐Saji

Bibliographic record

VenueJournal of Aesthetics and Art Criticism · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicFeminist Epistemology and Gender Studies
Canadian institutionsConcordia UniversityMcGill University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsRacializationExhibitionArtPhenomenology (philosophy)SculptureExposition (narrative)PaintingVisual artsArt historyFine artAestheticsWhite (mutation)SociologyLiteratureRace (biology)Gender studiesPhilosophy

Abstract

fetched live from OpenAlex

I develop a phenomenological account of racialized encounters with works of art and film, wherein the racialized viewer feels cast as perpetually past, coming “too late” to intervene in the meaning of her own representation. This points to the distinctive role that the colonial past plays in mediating and constructing our self‐images. I draw on my experience of three exhibitions that take Muslims and/or Arabs as their subject matter and that ostensibly try to interrupt or subvert racialization while reproducing some of its tropes. My examples are the Jean‐Joseph Benjamin‐Constant exhibition at the Montreal Museum of Fine Arts (2015), the exposition Welten der Muslime at the Ethnologisches Museum in Berlin (2011–2017), and a sculpture by Bob and Roberta Smith at the Leeds City Art Gallery, created in response to the imperial power painting, General Gordon's Last Stand, that is housed there. My interest is in how artworks contribute to the experience of being racialized in ways that not only amplify the circulation of images but also constitute difficult temporal relations to images. Drawing on Frantz Fanon's Black Skin, White Masks, I argue that such racialized images are temporally gluey, or stuck, so that we are weighted and bogged down by them.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.639
Threshold uncertainty score0.264

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.021
GPT teacher head0.340
Teacher spread0.319 · 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

Citations37
Published2019
Admission routes3
Has abstractyes

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