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Record W4403092913 · doi:10.1016/j.gfs.2024.100808

Disparities and determinants of Somali refugee food security in Nairobi, Kenya

2024· article· en· W4403092913 on OpenAlexafffund
Zack Ahmed, Jonathan Crush, Samuel Owuor, Elizabeth Opiyo Onyango

Bibliographic record

VenueGlobal Food Security · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicMigration and Labor Dynamics
Canadian institutionsUniversity of AlbertaBalsillie School of International Affairs
FundersSocial Sciences and Humanities Research CouncilSocial Sciences and Humanities Research Council of Canada
KeywordsSomaliFood securityRefugeeGeographySocioeconomicsEnvironmental healthDevelopment economicsPolitical scienceEconomicsMedicineAgriculture

Abstract

fetched live from OpenAlex

• Most Somali refugee households in Eastleigh, Nairobi, report that their food security situation has improved compared to conditions in Somalia. However, almost 40% of households are severely food insecure. • Food security generally improves over time for Somali refugees as they acclimate to their new environment, though recent migrants face heightened risks of food insecurity. • Key factors influencing food security include household income, the education level of the household head, employment status, and gender. • There are important spatial disparities within the Eastleigh district, with different sections showing varying levels of food security, dietary diversity, and household income. • While many households receive remittances, the paper finds no significant impact of remittance receipt on food security; however, food-secure households are more likely to send remittances to Somalia.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.190
Threshold uncertainty score0.378

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.013
GPT teacher head0.298
Teacher spread0.285 · 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 source (direct Gemma or distilled Codex), not a consensus.

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

Citations8
Published2024
Admission routes2
Has abstractyes

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