Notes toward a Virtual Poetics: An Essay on Solaris, Assemblage, and Blackness
Bibliographic record
Abstract
My inquiry aims to recast the media of new media studies both as an interface and a racialized site of violation. I call on the writings of artists, theorists, and social scientists to redefine this word; to explore the ways the language of new media and the virtual spaces they occupy might describe an emergent, politically charged poetics. As a primary text, I examine Stanisław Lem’s Solaris (and Andreĭ Tarkovskiĭ’s adaptation), whose black ocean resists actively the scientists’ attempts to apprehend or describe it. Both novel and film, as with the Solarian surface itself, perform, I argue, an opaque, Virtual Poetics—a generically unstable aesthetic model at once of, but not bound exclusively to, Black aesthetics. More critically, I point to a racially charged figure in Lem’s text, who doubly mediates the colonial pasts endemic to the genre, in order to establish the political stakes for artists who commit to opacity as an aesthetic principle. I set the scholarship of Neda Atanasoski and Kalindi Vora, Simone Browne, Lisa Nakamura, Saidiya Hartman, and Julia Kristeva in conversation with art-objects by poet Dolores Dorantes and visual artist Jeron Braxton to provide a grammar with which we might approach this poetics.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.017 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".