Interpellation of warfare consumers through the concept of ‘terrorism’: an Althusserian perspective
Bibliographic record
Abstract
The existing critical literature has pinpointed how the concept of ‘terrorism’ has successfully been shaped as a powerful imperialist tool, especially since 9/11. This article adopts an Althusserian approach to examine how the imperialist concept of terrorism functions socially through a process of interpellation that leads privileged individuals to perceive themselves as ‘potential victims’ but ultimately positions them as consumers of warfare. This interpellation reinforces an idealist narrative that frames history as a relentless struggle of man against man, rather than as a social story shaped by historical relations of production. From an Althusserian perspective, human beings are social beings within a mode of production geared towards the profit of an imperialist elite. Furthermore, since the onset of capitalism’s structural crisis in the 1970s, militarism and war-in-itself have become fundamental to the accumulation of monopoly-financial capital by the imperialist elite. In this context, terrorism is ideologically deployed to construct a division between the West and ‘others,’ justifying endless warfare while diverting attention from the underlying class contradictions of imperialism. The perpetual ‘war on terror’ has become a spectacle, obscuring individuals’ class positions and the reality of global militarization in everyday life.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.067 |
| Scholarly communication | 0.010 | 0.015 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".