Experiences of refugees: Understanding challenges of Eritrean refugees, Alemwach site, Ethiopia
Bibliographic record
Abstract
Ethiopia has progressive refugee policies and proclamation, particularly the “out of camp policy,” that allows Eritrean refugees to live outside of designated refugee camps. However, the implementation of these policies has been problematic, leading Eritrean refugees in Ethiopia to face significant barriers. The purpose of this research was to understand the specific difficulties faced by Eritrean refugees in the Alemwach site, Ethiopia. An ecological system and human rights framework were used to understand the challenges. A qualitative case study design was employed to explore the challenges. The study employed a purposive snowball sampling technique to select 10 participants. In-depth interviews, observations, and documents were used for data collection. The analysis of this data identified six themes that capture the complex and multifaceted challenges experienced by Eritrean refugees in Ethiopia: ongoing psychosocial challenges due to forced displacement; freedom of movement; the right to work; relationship with the host communities; security and crime; and document restoration and vital life events registration. Effective strategies must be developed to mitigate these challenges and align policy implementation with the realities of refugee experiences. The findings have implications for social work, psychology, and law.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.017 | 0.011 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 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".