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Record W7117686544 · doi:10.55016/ojs/tsw.v3i2.80134

Experiences of refugees: Understanding challenges of Eritrean refugees, Alemwach site, Ethiopia

2025· article· W7117686544 on OpenAlexaff
Jibril Hassen, Kamal Khatiwada

Bibliographic record

VenueTransformative Social Work · 2025
Typearticle
Language
FieldPsychology
TopicMigration, Health and Trauma
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsRefugeeSnowball samplingQualitative researchNonprobability samplingHuman rightsFace (sociological concept)Displaced personHuman security

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.300
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.052
GPT teacher head0.368
Teacher spread0.316 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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