Forced Displacement and the Crisis of Citizenship in Africa’s Great Lakes Region: Rethinking Refugee Protection and Durable Solutions
Bibliographic record
Abstract
This article explores refugee protection and durable solutions in Africa’s Great Lakes region by examining conflict, displacement, and refugees in the light of the crisis of citizenship. Drawing on empirical data from nine studies across the region, we scrutinize the causes of conflict and displacement and refugee policies and practice in the region through the lens of citizenship. First, we argue that the continued plight of many refugees in the region without durable solutions results, at least in part, from an endemic and systemic inability of many people in the region to realize citizenship in a meaningful way. This inability, we argue, is a significant contributor to the continued forced displacement of millions of people, with many still refugees, even after living in the host states for over three decades. Second, we argue that solutions are failing because discussions about the root causes of refugee influxes and movements often fail to capture the intricately connected historical, political, social, economic, religious, and legal factors that engender displacement. We submit that full and equal enjoyment of the rights and benefits of citizenship by all, including access to citizenship for refugees, is one means of resolving displacement and providing durable solutions to refugees.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.021 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.002 | 0.004 |
| 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".