Insecurity and the reintegration of former armed non-state actors in Colombia
Bibliographic record
Abstract
Abstract In this paper, we focus on the completion of a government reintegration program in Colombia for former non-state armed actors, such as rebel forces and militias, in the post-conflict period. As the members of these groups lay down their arms and return to a peaceful existence, the effectiveness of their transition to ‘normal’ lives can be critical in preventing the re-emergence of conflict and violence. Former combatants face numerous challenges and hardships such as criminal violence, political violence, economic hardship that, if not properly addressed, may increase the likelihood that some of them become involved in criminal work, political violence, or other activities that undermine peace. We develop a theory of the impact of violence and insecurity challenges facing former, non-state armed actors (henceforth, ANSAs). We suggest that the numerous challenges involved in leading a normal life under conditions of abnormal security will likely make successful completion of government reintegration programs more difficult for ANSAs. We also consider and account for the powerful effects of gender and family in the successful completion of a reintegration program. We test our theoretical model on the successful completion of a government reintegration program in Colombia, and test our hypotheses on a large database of ANSAs. We find support for our hypotheses, as well as social factors that greatly influenced the likelihood of successful completion of the Colombian government’s reintegration program.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".