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Record W4400829558 · doi:10.1139/facets-2023-0111

Did the COVID-19 pandemic disrupt food security in West African rural communities? Survey results from four regions of Senegal and Burkina Faso

2024· article· en· W4400829558 on OpenAlexafffundvenue
Etienne Quillet, Isabelle Vandeplas, Katim Touré, Safiétou Sanfo, Fatoumata Lamarana Baldé, Liette Vasseur

Bibliographic record

VenueFACETS · 2024
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicCOVID-19 Pandemic Impacts
Canadian institutionsBrock University
FundersInternational Development Research Centre
KeywordsFood securityConsumption (sociology)Market accessBusinessAgricultureGeographySocioeconomicsEconomic growthDevelopment economicsEconomics

Abstract

fetched live from OpenAlex

Transboundary rural communities in West Africa play an important role in the exchange of goods, mainly food, among countries. The COVID-19 pandemic restricted these activities due to the closure of the borders. Semi-structured interviews were conducted in two regions of Senegal and Burkina Faso to examine the impacts of these restrictions on the pillars (availability, access, utilization, and stability) of food security in rural areas on men and women. The data set included 230 interviews, and they were analyzed through thematic content analysis. The results showed a decrease in agricultural production in all the communities due to mainly lack of labor force, and limited access to inputs, resulting in increased post-harvest losses. The disruption of trade and border and market closures affected rural families engaged in transboundary trade. Farmers experienced a sharp loss of household income leading to debts and decapitalization. Availability and diversity of and access to food was also heavily affected. Food security greatly varied among the communities and between countries. Perceptions also varied between men and women in terms of production, mobility, and food consumption. The restriction measures have triggered a spiral of effects and responses seriously impacting long-term food security in already highly vulnerable countries.

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)0.002
metaresearch head score (Gemma)0.002
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 score0.053
Threshold uncertainty score0.957

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
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 integrity0.0000.001
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 head0.125
GPT teacher head0.300
Teacher spread0.175 · 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

Citations2
Published2024
Admission routes3
Has abstractyes

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