The Art of (Bio)Surveillance: Bioart and the Financialization of Life Systems
Bibliographic record
Abstract
Our paper examines the complex relations between bioart and the financialization of life itself through the bioeconomic apparatus of biosurveillance. Briefly, the bioeconomy, or the relocation of genetic, microbial and cellular productive processes within capitalism (see Melinda Cooper 2008), involves the expansion of the life sciences industries into every domain of society. Bioart explores the “mobilization of the biological” to understand how culture confronts and/or collaborates with neoliberal forces. One key aspect of the bioeconomy is the financialization of living systems via the increasing importance of financial markets within the biotechnology industry and post-genomic technologies (synthetic biology). The concept of biosurveillance, originally developed for disease surveillance and monitoring, is now, in the era of the post-terror state, strongly linked to national security; it concerns “a wider biopolitical strategy” connected to the active gathering and surveillance of “person-specific biological information” (Parry 2012: 718). The use of biosurveillance to secure populations is aimed at securitizing populations in the context of both defence and financialization. Major science and technology innovations (RFID tags, VeriChips, animated tattoos, DNA chips, human barcodes, etc.), which enable the policing of “biological threats” have become integrated into the bioeconomic cultural apparatus and have inspired several bioartists. What does bioart uncover about the complex relations between risk-based surveillance and the accumulation of insecurity in an era of financialized capital?
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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".