Next Generation Disarmament, Demobilization and Reintegration
Bibliographic record
Abstract
The process of disarming, demobilizing and reintegrating ex-soldiers at conflict’s end is as old as war itself. The results of these efforts are far from even. Even so, disarmament, demobilization and reintegration (DDR) has assumed a central place in the imagination of the peace, security and development communities. It is frequently advanced as a key pillar of multilateral and bilateral stabilization and reconstruction efforts at war’s end. Yet, the contexts in which DDR is conducted are also changing. As the United Nations and others grapple with the new geographies of organized violence, it is hardly surprising that they are also adapting their approaches. Organizations operating in war zones (and also outside of them) are struggling to identify ways of ‘disengaging’ Al Shabaab in Somalia or northern Kenya, Jihadi fighters in Syria and Iraq, Taliban remnants in Afghanistan and Pakistan, and Boko Haram militia in Nigeria. There are increasingly complex legal and operational challenges for those involved in DDR about when, how and with whom to engage. In order to effectively engage with these dilemmas, this article considers the evolving form and character of DDR programs. In the process, it considers a host of opportunities and obstacles confronting scholars and practitioners in the twenty first century, offering insights on future trajectories.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".