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Record W7057277850

Impact of the Russia-Ukraine war on Africa: Policy implications for navigating shocks and building resilience

2024· other· en· W7057277850 on OpenAlexfundno aff

Bibliographic record

VenueEconstor (Econstor) · 2024
Typeother
Languageen
FieldEngineering
TopicParticle accelerators and beam dynamics
Canadian institutionsnot available
FundersConsortium pour la recherche économique en AfriqueEconomic Research ForumInternational Development Research Centre
KeywordsResilience (materials science)Food securityEconomic impact analysisPsychological resilienceTransmission channelShock (circulatory)Vulnerability (computing)Econometric modelNatural disasterGovernment (linguistics)
DOInot available

Abstract

fetched live from OpenAlex

This report synthesises the evidence on the impact of the war and its implications on food security in Africa based on country case studies covering Egypt, Ethiopia, Kenya, Morocco, Mozambique, Senegal, South Africa and Sudan, as well as Africa-wide studies utilising econometric modelling techniques. The studies examine the transmission channels of the impact of the Russia-Ukraine war on African economies and their resilience. The report finds that while direct trade exposure is low, Africa relies heavily on Russia and Ukraine for food and fertiliser imports.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.101
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.013
GPT teacher head0.282
Teacher spread0.269 · 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 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

Citations1
Published2024
Admission routes1
Has abstractyes

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