Entangled Militarisms: The “Balakot Airstrike” and Circulating Technologies of Violence in/between India and Israel
Bibliographic record
Abstract
Abstract In February 2019, Indian warplanes struck so-called terrorist camps in Pakistan in what became known as the “Balakot airstrike.” A key feature of this episode was the use and legitimation of Israeli-origin and -supplied technologies of violence by Indian actors who proudly proclaimed India’s likeness to Israel. This article focuses on the Balakot airstrike to examine the understudied relations between Indian and Israeli militarisms. Responding to Eurocentric and methodological nationalist tendencies in scholarship on militarism and the arms trade, this article advances the concept of entangled militarisms to characterize the role of Israel in Balakot and the India–Israel relationship more broadly. This concept places emphasis on four aspects of militarism, recognizing it as transnational, processual, constituted through discourse-materiality, and forming broader assemblages of violent ordering. Through this lens, I map an expansion in recent years of India and Israel’s arms trade, partnerships in weapons development and manufacturing, and security personnel trainings. Locating it within these transnational processes of the production, circulation, use, and legitimation of technologies of violence, the Balakot airstrike is understood as a moment of intensifying entanglements of militarisms. This, in turn, illuminates how entangled militarisms in/between India and Israel are constitutive of a wider assemblage of violent global ordering.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.029 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".