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Record W3216757435 · doi:10.3390/su132212888

The Effects of Pandemics on the Vulnerability of Food Security in West Africa—A Scoping Review

2021· article· en· W3216757435 on OpenAlexafffund
Liette Vasseur, Heather VanVolkenburg, Isabelle Vandeplas, Katim Touré, Safiétou Sanfo, Fatoumata Lamarana Baldé

Bibliographic record

VenueSustainability · 2021
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicCOVID-19 Pandemic Impacts
Canadian institutionsBrock University
FundersInternational Development Research Centre
KeywordsFood securityVulnerability (computing)PandemicUnrestAgricultureDevelopment economicsBusinessGeographyRural areaEconomic growthEnvironmental healthSocioeconomicsEnvironmental planningCoronavirus disease 2019 (COVID-19)Political scienceDiseaseEconomicsMedicineComputer securityInfectious disease (medical specialty)

Abstract

fetched live from OpenAlex

The purpose of this paper was to show the effects of the Ebola and COVID-19 pandemics on food security vulnerability in West Africa. The methodology is based on a scoping literature review using the PRISMA method. The study showed that food security was affected by the restrictive measures in the different West African countries. In addition, it shows that this region is highly vulnerable to such crises, which can combine their effects with those of other events such as climate change and civil unrest. In both pandemics, all pillars of food security were affected. The effects on urban and rural centers may be very different. The study suggests a better understanding of the differences between rural and urban centers and between men and women and how long-term restraint measures can affect rural areas where agriculture is the main lever for reducing food insecurity. Food security must be seriously considered by governments when implementing restrictive measures during a pandemic. Consideration of health factors alone at the expense of food security can greatly exacerbate health problems and even increase cases of disease.

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)0.006
metaresearch head score (Gemma)0.063
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
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 score0.494
Threshold uncertainty score0.945

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.063
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.031
GPT teacher head0.291
Teacher spread0.260 · 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 designTheoretical or conceptual
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

Citations10
Published2021
Admission routes2
Has abstractyes

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