Resettlement of internally displaced persons (IDPs) in Nigeria: the housing problems facing IDPs in Abuja camps and the risk of homelessness and secondary displacement
Bibliographic record
Abstract
Internal displacement remains the worst humanitarian crisis facing Nigeria, with an estimated 2.7 million persons displaced in the North-East at the end of 2020 as a result of conflicts. Many of these internally displaced persons (IDPs) migrated to Abuja where they live in dilapidated homes and are at constant risk of becoming homeless because the government has no existing plan for IDPs’ resettlement. This study aimed to explore the housing challenges of IDPs in Abuja and how they can lead to secondary displacement. Data was collected from 38 IDPs using qualitative interviews and the collected data were analysed thematically with the aid of NVivo 12. Results revealed that the current housing conditions of the IDPs expose them to many diseases; the IDPs are at risk of secondary displacement which may endanger all the progress, development, and resilience build over the years. This points to a need for proper resettlement of the IDPs and highlights the need and roles of social workers in Nigeria. The study reveals how social workers can advocate for access to culture-sensitive and self-sustaining targeted resources, contribute to proper IDPs’ resettlement and reintegration, and influence the amendment and adoption of the 2003 National Policy on Internal Displacement.
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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 | 0.006 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".