Reintegrating Members of Armed Groups into Society: An Evaluation of Three Approaches
Bibliographic record
Abstract
Disarmament, Demobilization, and Reintegration (DDR) programs have become a staple feature of modern peacekeeping missions. These programs combine individual, group and state-level interventions in an effort to create the conditions necessary for peaceful transitions in post-conflict societies. This study examined quantitative data from a longitudinal data set on 741 Haitian men from who participated in or qualified to participate in DDR programs after the 2004 coup. Interviewed at baseline, six months later, and then every twelve months for six years, the participants’ behaviors and attitudes were examined to determine the success of components in three types of DDR approaches and to determine which of the three interventions was most effective at reducing criminal behavior and increasing pro-social behaviors of participants. A Linear Mixed Model was used to examine the impact of education (both classical and vocational) as well as participation in organized sports and recreation activities on a variety of outcome measures including engagement in crime, socializing with armed peers, family functioning, and engagement in non-violent methods of civic participation. Both types of education were found to have a positive impact on self-efficacy and internal locus of control (which are tied to an individual’s sense of control over their own life and future). Furthermore, they decreased involvement in crime, violence, and association with armed peers, and an improvement in family functioning, the ability to make decisions, involvement in volunteering, and non-violent political engagement. Being involved in sports and recreation was similarly associated with a decrease in violence and crime and a decrease in associating with armed peers. This findings support criminological research which suggests education has a positive impact on decreasing crime amongst those who have committed crimes in the past. This also makes a small contribution to establishing an evidence base for DDR interventions, which are widely seen by policy makers as being an essential part of the peacekeeping process in some types of conflict but for which there has been little empirical evidence of their effectiveness. Suggestions for future research and social work practice implications are also addressed including the impact of education on identity creation/reformation.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".