Performativity without theatricality: experiments at the limit of staging AI
Bibliographic record
Abstract
Referencing participant observation in a research-creation lab devoted to performance and artificial intelligence (AI), this article summarizes and intervenes within two discourses surrounding the performativity of computation. I first summarize the media-theoretical debate over whether or not electronic computation counts as what J. L. Austin and Jacques Derrida defined as ‘performative’. This turns out to be a divide over the politics of theoretical analysis, and as such these positions can be synthesized together. Relying on Samuel Weber’s concept of ‘theatricality’, I set out a novel proposal for understanding computation as representing a limit of performativity without theatricality. Secondly, I review the experiments conducted with staging recent machine-learning models within the University of Toronto’s BMO Lab. A scholarly tradition distinct from the above has turned to a ‘metaphysical performativity’, describing all reality as performatively animate rather than representational and inert; some have pointed to recent AI developments as a demonstration of the truth of this view. I dissent, with evidence from the aesthetic experience of watching AI performance. Finally, I critique the ideology implicit in theories that take the appearance of AI animacy as a model for social reality.
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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 | 0.008 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 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".