Performativity without theatricality: experiments at the limit of staging AI
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.
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.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it