Biometrics: Intersecting borders and bodies in liberal bionetwork states
Bibliographic record
Abstract
Abstract This paper addresses the following two part question: How are liberal bionetwork states using technology to manage human flows and what are the broader political implications of these developments? Specifically, I examine the expanding scope and implications of biometric surveillance technologies in liberal states’ border securitization practices. Since 9/11, biometric identification systems have been fast‐tracked as a “silver‐bullet solution” to address perceived threats to border security in the United States and the European Union. I argue that the deployment of new information and biometric technologies in the United States and the European Union is both individuating and expanding state space into new geographies (i.e., state control is extensifying inside bodies and reaching outside sovereign territorial boundaries). This is due, in part, to the deployment and growing interoperability of new border securitization technologies, like US‐VISIT in the United States and the second generation of the Schengen Information System (SIS II) and Visa Information System (VIS) in the European Union. Biometric systems augment efforts to create a high‐concept, multi‐layered, interoperable system of “virtual borders” designed to deter or intercept determined terrorists, criminals, and unauthorized migrants. As the deployment of biometric technologies creep from the margins to the mainstream, privacy and surveillance concerns will become evermore salient. Equally important are the implications of this incremental “disembodied integration” of people with states on how liberal bionetwork societies make decisions about belonging and exclusion. As virtual borders extend into our bodies and around the globe, securitization measures that intersect bodies and states require a virtual theory to account for these new geographies of state space and the qualitative affects on individuals.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".