Beyond Classification: The Machinic Sublime
Bibliographic record
Abstract
Beyond Classification: The Machinic Sublime (BCMC) emulated an academic roundtable discussion with the authors and 3 machinic/more-than-human guests. Part performance, part intervention within the context of an academic conference, BCMC introduces a novel and explicitly visible strategy of co-dependency for an array of diverse intelligences through a connected loop of human, machine, and animal agencies. The meteoric rise of AI in the last years can be seen as a part of a larger tendency towards deeper, more opaque data collection and analysis techniques that form the dense substratum beneath the proliferation of human-computer interfaces today. As a human developer, the most striking qualities of generative AI are its vastness, non-determinism, and infinitude— explicit themes and qualities of a machinic ‘sublime’. How can a human artist/programmer sensibly navigate this multi-dimensional space of latent meaning? This intervention is an experimental roundtable discussion/performance via web conferencing, a new kind of Turing Test where success in the testing is not found in the plausible simulation of human consciousness through speech, but rather in expressing diverse intelligences through new forms of language. In this multi-agent exchange, human interlocutors and non-human partners argue the possibility of a machinic sublime. Together, these interlinked discussions become an emergent system. In this roundtable format, audience interventions are welcome.
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 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 | 0.005 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.013 |
| Scholarly communication | 0.020 | 0.018 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.107 | 0.034 |
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".