Hybrid Warriors and the Formation of New War Masculinities: A Case Study of Indonesian Foreign Fighters
Bibliographic record
Abstract
At the heart of new wars are economic structures, patterns of violence and formations of collective meaning, which appear to blend localised and globalised practices of gender. While new wars appear to mirror the kind of warrior masculinity that preceded the modern state, they also draw on new technologies and symbolism to give meaning to acts of war. In the case of foreign fighters, armed groups increasingly draw on globalised cultural products (film, electronic publications and images) to entice volunteers to fight on the battlefields of the 21st century. The use of masculine models and gendered discourses to recruit men to fight in these conflicts has been well studied. However, the process through which ‘local’ and ‘global’ practices of gender are blended by highly mobile fighters to forge the practices of new war has received far less attention. Drawing on the notion of cultural hybridity, this article asks how interactions between different configurations of gender make new wars possible. To do this, it empirically explores encounters between notions of militarised manhood through the lives of four Indonesian former foreign fighters. By utilising life history interviews, this article makes the case that the masculinity of these ‘new warriors’ relied on the tensions between, and synthesis of, anti-colonial notions of organised violence that are rooted in Indonesian history and globalised jihadi discourse on 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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.018 | 0.010 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".