A Corrective for Cultural Studies: Beyond the Militarization Thesis to the New Military Intelligence
Bibliographic record
Abstract
This paper theorizes a new military intelligence and offers a modest corrective to the orthodoxy of the militarization thesis prevalent in cultural studies and the critical human sciences. The biopolitical orientation of population-centric counterinsurgency (COIN) warfare in Afghanistan reveals the multidirectional travel of rationalities and forms of coherence between modern liberal ways of rule and Western-bloc expeditionary ways of war. Through the work of Michel Foucault, and drawing on Michael Dillon and Julian Reid’s analysis of the biopoliticization of war (2008), COIN is interrogated as a continuation of biopolitics by other means. Conceptualizing a continuum of fast and slow military violence to produce islands of security and stability, COIN generates a mix of persuasive material forces that, while not kinetic or combat-oriented, are internal to the battlespace of military warfighting. The aim of this theoretical intervention is to trouble our understanding of military violence and power. Rather than subscribe unconditionally to the idea of a domineering military contaminating domestic civilian environments, the paper establishes a different trajectory: perhaps there is always-already a spirit of counterinsurgency internal to the art of biopolitical governmentality, which in turn conjugates contemporary military ways of war.
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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 | 0.022 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.008 | 0.098 |
| Scholarly communication | 0.010 | 0.021 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.004 | 0.021 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".