Civil war as a social process: actors and dynamics from pre- to post-war
Bibliographic record
Abstract
What accounts for overarching trajectories of civil wars? This article develops an account of civil war as a social process that connects dynamics of conflict from pre- to post-war periods through evolving interactions between nonstate, state, civilian, and external actors involved. It traces these dynamics to the mobilization and organization of nascent nonstate armed groups before the war, which can induce state repression and in some settings escalation of tensions through radicalization of actors, militarization of tactics, and polarization of societies, propelled by internal divisions and external support. Whether armed groups form from a small, clandestine core of dedicated recruits, broader networks, social movements, and/or fragmentation within the regime has consequences for their internal and external relations during the war. However, not only path-dependent but also endogenous dynamics shape overarching trajectories of civil wars. During the war, armed groups develop cohesion and fragment in the context of evolving internal politics, including socialization of fighters, institution-building in the areas that they control, which civilians can collectively resist, competition and cooperation with other nonstate and state forces, and external influence. After the war, armed groups transform to participate in continuing conflict and violence in different ways in interaction with multiple actors. This analysis highlights the contingency of civil wars and suggests that future research should focus on how relevant actors form and transform as they relate to one another to understand linkages between conflict dynamics over time and on continuities and discontinuities in these dynamics to grasp overarching trajectories of civil wars.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.013 |
| Scholarly communication | 0.009 | 0.014 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".