Implementing DDR in Settings of Ongoing Conflict: The Organization and Fragmentation of Armed Groups in the Democratic Republic of Congo (DRC)
Bibliographic record
Abstract
Although it is common for armed groups to splinter (or “fragment”) during contexts of multi-party civil war, current guidance on Disarmament, Demobilization, and Reintegration (DDR) does not address the challenges that arise when recalcitrant fighters, unwilling to report to DDR, break ranks and form new armed groups. This Practice Note addresses this issue, drawing lessons from the multi-party context of the DRC and from the experiences of former members of three armed groups: the Rally for Congolese Democracy-Goma (RCD-Goma), the National Congress for the Defense of the People (CNDP), and the DRC national army (FARDC). While the findings indicate that the fragmentation of armed groups may encourage desertion and subsequent participation in DDR, they also show that active armed groups may monitor DDR programs and track those who demobilize. Remobilization may follow, either as active armed groups target ex-combatants for forced re-recruitment or as ex-combatants remobilize in armed groups of their own choice. Given these dynamics, practitioners in settings of partial peace may find it useful to consider non-traditional methods of DDR such as the use of mobile patrols and mobile disarmament units. The temporary relocation of ex-combatants to safe areas free from armed groups, or to protected transitional assistance camps, may also help to minimize remobilization during the reintegration phase.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".