Anthropology of security and security in anthropology: Cases of counterterrorism in the United States
Bibliographic record
Abstract
In this article we propose a mode of analysis that allows us to consider security as a form distinct from insecurity, in order to capture the heterogeneity of security objects, logics and forms of action. We first develop a genealogy for the anthropology of security, demarcating four main approaches: violence and state terror; military, militarization, and militarism; para-state securitization; and what we submit as ‘security assemblages.’ Security assemblages move away from focusing on security formations per se, and how much violence or insecurity they yield, to identifying and studying security forms of action, whether or not they are part of the nation-state. As an approach to anthropological inquiry and theory, it is oriented toward capturing how these forms of action work and what types of security they produce. We illustrate security assemblages through our fieldwork on counterterrorism in the domains of law enforcement, biomedical research and federal-state counter-extremism, in each case arriving at a diagnosis of the form of action. The set of distinctions that we propose is intended as an aid to studying empirical situations, particularly of security, and, on another level, as a proposal for an approach to anthropology today. We do not expect that the distinctions that aid us will suffice in every circumstance. Rather, we submit that this work presents a set of specific insights about contemporary US security, and an example of a new approach to anthropological problems.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.021 | 0.038 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".