A Call to Otherness: Inscribing Digital Vernaculars into the Art Institution
Bibliographic record
Abstract
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.
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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 | 0.012 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.016 | 0.067 |
| Scholarly communication | 0.029 | 0.023 |
| Open science | 0.002 | 0.021 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".