Second Generation Biometrics and the Future of Geosurveillance: A Minority Report on FAST
Bibliographic record
Abstract
Biometrics are technologies that measure the body and are typically seen as existing for the purposes of identity verification. However, they are rapidly moving towards a new paradigm of behavioural analysis and prediction. The Department of Homeland Security’s Future Attribute Screening Technology (FAST) is one example of this shift. In this article, we use FAST to explore the implications of new biometric technologies for geosurveillance. We argue that second generation biometrics mark a major shift in the application of geosurveillance due to their spatial and topological nature, and that they are motivated in part by a desire to make bodies more legible. More importantly, we argue that second generation biometrics both intensify and extend geosurveillance of already marginalized bodies. Finally, we call for more geographical research into biometrics given their rapid development and oncoming proliferation.
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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 | 0.001 | 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 | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 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".