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Record W4389427090 · doi:10.21083/ajote.v12i2.7516

Access and politics of higher education for refugees: Comparative contexts from Uganda and Ethiopia

2023· article· en· W4389427090 on OpenAlexvenueno aff
Emnet Tadesse Woldegiorgis, Kennedy Monari

Bibliographic record

VenueAfrican Journal of Teacher Education · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicEducation and experiences of immigrants and refugees
Canadian institutionsnot available
Fundersnot available
KeywordsRefugeePoliticsPolitical scienceLegislatureSettlement (finance)Economic growthGender studiesSociologyLaw

Abstract

fetched live from OpenAlex

An intricate mesh of factors hampers students from refugee backgrounds from accessing and having success in higher education (HE). The paper examines HE within a broader framework of refugee education and the future politics of its provision. Much research is done on refugee children and youth in schooling contexts, but less is known about students from refugee backgrounds in HE. According to the United Nations High Commissioner for Refugees (UNHCR), an estimated 65 million people are currently displaced, of whom over 21 million meet refugee status criteria. Nevertheless, only five percent of this group has access to HE. Thus, access to HE and the success of students from refugee backgrounds are central to the discussion on the future of HE. The paper provides a comparative overview of difficulties regarding access to HE for refugee students in Uganda and Ethiopia, highlighting policy and settlement issues in their legislative and political contexts. It also interrogates students’ coping mechanisms, exploring their experiences through interviews. The study uses secondary data, document analysis, and interviews with a total of 30 students from refugee backgrounds, fifteen from Makerere University in Uganda and fifteen from Addis Ababa University in Ethiopia.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.519
Threshold uncertainty score0.234

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.055
GPT teacher head0.436
Teacher spread0.381 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations3
Published2023
Admission routes1
Has abstractyes

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