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Record W4383068923 · doi:10.1007/s11125-023-09645-w

The real cost of teaching in a refugee camp: Asking the difficult questions

2023· article· en· W4383068923 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueProspects · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicEducation and experiences of immigrants and refugees
Canadian institutionsUniversité du Québec à Montréal
FundersUnited Nations High Commissioner for RefugeesNational University of Ireland, Maynooth
KeywordsRefugeeAccreditationSociologyPedagogyMedical educationShort coursePolitical sciencePublic relationsLawMedicine

Abstract

fetched live from OpenAlex

Abstract This article asks difficult questions about higher-education courses provided by Western institutions to people living in refugee camps. It critically examines a blended-learning approach that incorporates a massive online open course (MOOC) into a scaffolded higher-education program—the University of Geneva’s Connected Blended Learning model—in the Kakuma refugee camp. It assesses the effectiveness of this approach in an accredited University of Geneva human-rights law course, which ran in the Kakuma camp from 2017 to 2020. On the basis of the long experience of the course leaders and research carried out with students who participated in the course, the article explores ways of improving this model by answering difficult questions about the real cost of teaching in a refugee camp. This paper was co-written by a professor, a researcher, and students who were involved in the course, two of whom are refugees living in Kakuma refugee camp.

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.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.685
Threshold uncertainty score0.984

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.019
GPT teacher head0.363
Teacher spread0.344 · 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