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 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 | 0.013 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.018 | 0.012 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.003 | 0.005 |
| 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".