Demobilising and Disengaging Violent Extremists: Towards a New UN Framework
Bibliographic record
Abstract
First and second generation programmes of Disarmament, Demobilisation and Reintegration (DDR), are no longer ‘fit for purpose’ in contexts of violent extremism. Recognising this, voices from within the United Nations (UN) system have recently called for the development of a practice framework combining DDR and Countering Violent Extremism (CVE). Drawing on examples from Nigeria and Somalia, this commentary outlines six issue areas where DDR and CVE overlap, and where further operational guidance is required. These issue areas are: safe passage; the handling of seized and captured weapons; risk assessment; the use of deradicalisation programmes; the reintegration of extremist offenders; and the links between DDR and rehabilitation programmes for extremist prisoners.
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 | 0.018 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.006 | 0.025 |
| Scholarly communication | 0.015 | 0.016 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.011 | 0.010 |
| Insufficient payload (model declined to judge) | 0.003 | 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".