MétaCan
Menu
Back to cohort
Record W3175678904 · doi:10.1386/jcs_00033_7

A Call to Otherness: Inscribing Digital Vernaculars into the Art Institution

2021· article· en· W3175678904 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Curatorial Studies · 2021
Typearticle
Languageen
FieldArts and Humanities
TopicMuseums and Cultural Heritage
Canadian institutionsQueen's University
Fundersnot available
KeywordsExhibitionInstitutionThe artsSociologyRelevance (law)AestheticsVisual artsFrame (networking)ApprehensionInclusion (mineral)Contemporary artSpace (punctuation)Media studiesArtSocial scienceArt historyEpistemologyComputer sciencePolitical scienceLawPerformance artPhilosophy

Abstract

fetched live from OpenAlex

In the mid-2010s, a number of renowned museums and galleries across the world held retrospective exhibitions positioning digital arts within western art history. While inscribing some techno-aesthetic forms and behaviours into the contemporary arts institution, these exhibitions nevertheless cemented the exclusion of others. By examining the role and shortcomings of curatorial practices in this process, this article seeks to frame curating as an art of inclusion able to carve institutional and epistemic space for otherness. In doing so, I argue for the relevance of devices for noticing , defined as a range of tactics that enable the apprehension of digital vernaculars – everyday, ‘lower’ expressions of digital media culture – within institutional sites and discourses. Through these tactics, curators may provoke under-represented cultural actors, forms and behaviours into recognition, reverse the violence of institutional occlusion, and fertilize art histories.

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.

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)0.000
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.744
Threshold uncertainty score0.359

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.053
GPT teacher head0.279
Teacher spread0.226 · 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