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Record W4405379531 · doi:10.14426/ahmr.v10i3.2435

“An Endless Cycle of Worry and Hardship”: The Impact of COVID-19 on the Food Security of Somali Migrants and Refugees in Nairobi, Kenya

2024· article· en· W4405379531 on OpenAlexafffund
Zack Ahmed, Jonathan Crush, Bernard Owusu

Bibliographic record

VenueAFRICAN HUMAN MOBILITY REVIEW · 2024
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicCOVID-19 Pandemic Impacts
Canadian institutionsBalsillie School of International AffairsWilfrid Laurier University
FundersSocial Sciences and Humanities Research Council of CanadaInternational Fine Particle Research Institute
KeywordsSomaliWorryRefugeeFood securityCoronavirus disease 2019 (COVID-19)Political scienceDevelopment economicsGeographyEconomicsPsychologyMedicineLawAgriculturePhilosophy

Abstract

fetched live from OpenAlex

COVID-19 has produced unprecedented effects on the global economy and society by exposing multiple weaknesses and faultiness. The pandemic has disrupted global and local agricultural production processes and food supply chains with negative consequences for food security. Containment measures to limit the spread of COVID-19, including strict restrictions on the movement of people, goods, and services have affected urban food systems adversely in multiple ways. Urban migrants and refugees in many parts of the Global South have been disproportionately hit by these measures, increasing the precarity of their living conditions and exacerbating the food insecurity of the migrants’ households. Based on the results of a household survey and in-depth interviews with Somali migrants in Nairobi, Kenya in August 2022, this study documents the pandemic-related experience of these migrants in food access and consumption and assesses the overall impacts of COVID-19 on their food security. This study seeks to contribute to the emerging body of case study evidence that assesses the food security outcomes of the pandemic in vulnerable populations.

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)0.004
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.095
Threshold uncertainty score0.530

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.058
GPT teacher head0.338
Teacher spread0.280 · 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 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

Citations0
Published2024
Admission routes2
Has abstractyes

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