Terrorism in the Lake Chad Region: Integration of Refugees and Internally Displaced Persons
Bibliographic record
Abstract
The Lake Chad region is an intersection of four countries, namely Cameroon, Chad, Nigeria, and Niger, and has been a battleground of terrorism in recent years. While much is known about the devastating impact of the activities of Boko Haram, there is a dearth of empirical research on how individuals displaced by terrorism in the Lake Chad region have been integrated into new communities. Thus, the aim of this study was to explore the experiences of refugees and internally displaced persons (IDPs) regarding their integration into new communities in the Lake Chad region. The study adopted a qualitative design, that is, interviews and focus group discussions, to interact with participants from nine communities in Cameroon and Nigeria. Sixty-seven participants consisting of refugees, IDPs, host community leaders, and camp leaders were recruited to share their experiences. The study found similarities in the experiences of refugees and IDPs. Specifically, the study found that common identity (i.e. common culture and languages) enhanced social connection, safety, and integration of the refugees and IDPs into new communities. However, little has been done in terms of job creation, to enable refugees to have a source of livelihood, access to property, and essential services. The study has implications for policy-making in terms of governments in the Lake Chad region capitalizing on common identity and developing employable programs which will revitalize the economy of the region.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.006 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| 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".