‘Together at the Heart’: Familial Relations and the Social Reintegration of Ex-combatants
Bibliographic record
Abstract
Disarmament, demobilization, and reintegration (DDR) processes will often dismantle the command-and-control structures of non-state armed groups (NSAGs) to prevent possible remobilization. Recent studies demonstrate that in some cases ex-combatant networks provide important social and economic support that hasten transitions to civilian life; however, this literature focuses exclusively on networks that emerge among commanders, peers, and foot soldiers. In this article, we broaden existing literature on ex-combatant networks by examining the role that family relations play in combatants’ war and post-war trajectory. Drawing on 18 life history interviews with former male combatants from the Lord’s Resistance Army (LRA) we argue that the familial can often be as influential as peer-relations. Specifically, our study shows that, first, familiies can shape the defection, demobilization, and reintegration processes of ex-combatants, and, second, ex-combatant networks can play an important role in facilitating the reunion of families in the aftermath of war. The endurance of familial relations forged within NSAGs pose important considerations to DDR policies.
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 | 0.002 | 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 | 0.010 | 0.009 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| 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".