Climate, Insecurity and Displacement: Triple Barriers to the Reintegration of Former Boko Haram Associates
Bibliographic record
Abstract
Key Findings• In the Lake Chad Basin region, climate change, insecurity, and displacement are intertwined and work together to create severe challenges for communities who are already struggling in the midst of an ongoing conflict and humanitarian emergency.• Former associates of Boko Haram groups are facing unique challenges brought about by this interplay.They do not always have the same capacities to weather climate and other shocks due to lesser resources and access to adaptation strategies.Natural resource-based conflicts also affected their access to land.Moreover, women had even further limited access to input, finance, land, tools, and these strategies due to patriarchal familial and societal dynamics.• In comparison, unassociated peers could be -to various extents -more able to cope in response to these shocks.Less able to adapt and with communities unable to support them, former associates could engage in negative coping strategies such as restricting food consumption, sex work, or cutting trees to sell as firewood and charcoal.• Many people in the region have been forcibly displaced due to ongoing insecurity, including former associates.After exiting Boko Haram groups, they were often unable to return home and found themselves reintegrating into situations of displacement.Heightened insecurity meant that some experienced secondary -or multipledisplacements, and displaced ex-associates faced greater difficulties than those living in their home communities, due to language differences and lesser support from friends and relatives.• Former associates continued to receive threats from Boko Haram groups.Perceived as traitors, they were targeted, limiting livelihood options and freedom of movement, with men at higher risk of being killed and women of abductions and sexual violence.In response, women put themselves in danger by farming and collecting firewood in insecure areas to provide for families.Gender-based violence was also linked to insecurity in other ways, for example through abducted girls and women who had escaped being threatened by their 'husbands' that they would take them again.3 Background About MEACHow and why do individuals exit armed groups, and how do they do so sustainably without falling back into conflict cycles?These questions are at the core of UNIDIR's Managing Exits from Armed Conflict (MEAC) initiative.MEAC is a multi-year, multi-partner collaboration that aims to develop a unified, rigorous approach to examining how and why individuals exit armed conflict and evaluating the efficacy of interventions meant to support their transition to civilian life.MEAC seeks to inform evidence-based programme design and implementation in real time to improve efficacy.At the strategic level, the cross-programme, cross-agency lessons that will emerge from the growing MEAC evidence base will support more effective conflict resolution and peacebuilding efforts.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".