3x3x6 – 9 Sq.m. and 6 Surveillance Cameras
Bibliographic record
Abstract
The title of Shu Lea Cheang’s 3x3x6 which represented Taiwan at Venice Biennale 2019 derives from the 21st century high-security prison cell measured in 9 square meter and equipped with 6 surveillance cameras. As an immersive installation, 3x3x6 is comprised of multiple interfaces to reflect on the construction of sexual subjectivity by technologies of confinement and control, from physical incarceration to the omnipresent surveillance systems of contemporary society, from Jeremy Bentham’s panopticon conceptualized in 1791 to China’s Sharp Eyes that boasts 200 million surveillance cameras with facial recognition capacity for its 1.4 billion population. By employing strategic and technical interventions, 3x3x6 investigates 10 criminal cases in which the prisoners across time and space are incarcerated for sexual provocation and gender affirmation. The exhibition constructs collective counter-accounts of sexuality where trans punk fiction, queer, and anti-colonial imaginations hacks the operating system of the history of sexual subjection. This Image and Text piece intersperses images from the exhibition with handout texts written by curator Paul B. Preciado (against a grey background), as well as an interview between special section co-editor Paula Gardner and the artist that brings the extraordinary exhibition into further conversation with feminist technoscience scholarship. The project website is available at https://3x3x6-v2.webflow.io/.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.381 | 0.106 |
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".