Pathways to Violence in Civil Wars: Combatant Socialization and the Drivers of Participation in Civilian Targeting
Bibliographic record
Abstract
Abstract Recent research has drawn attention to the role of socialization in shaping the behaviors of rebel combatants during civil wars. In particular, scholars have highlighted how vertical and horizontal socialization dynamics can bring combatants to engage in a range of wartime practices, including the use of violence against civilians. This article synthesizes existing theories of combatant socialization and combines them into an integrated framework, which casts the focus on individual pathways toward civilian targeting and specifies the underlying sociopsychological mechanisms through which socializing influences motivate participation in violence. Specifically, the article charts five key pathways that operate through different mechanisms and that are based upon varying degrees of internalization regarding the legitimacy of civilian targeting. In each case, I also identify a number of unit-level factors that are likely to make a given pathway particularly prevalent among combatants. The article then illustrates how these pathways map onto the actual experiences of civil war combatants by examining the drivers of individual participation in violence against civilians among low-ranking members of the Revolutionary United Front in Sierra Leone. The case study evidence highlights the equifinal nature of violence perpetration during civil wars, shedding light on the different social needs, influences, sanctions, and constraints that may motivate involvement in violence. By analyzing rebel behavior through the prism of perpetrator studies, this article thus seeks to establish the civil war literature on firmer theoretical grounds, providing a synthetic account of the individual experiences, motives, and trajectories that are often left unaddressed in this body of research.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| 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".