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Record W3017324911 · doi:10.1111/cjag.12230

The COVID‐19 pandemic and agriculture: Short‐ and long‐run implications for international trade relations

2020· article· en· W3017324911 on OpenAlexaffvenue
William A. Kerr

Bibliographic record

VenueCanadian Journal of Agricultural Economics/Revue canadienne d agroeconomie · 2020
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicCOVID-19 Pandemic Impacts
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsDisequilibriumGlobalizationPandemicAgricultureSupply chainInternational tradeResilience (materials science)Coronavirus disease 2019 (COVID-19)BusinessPsychological resilienceConsumption (sociology)Development economicsEconomicsMarket economyGeographySociologyMarketing

Abstract

fetched live from OpenAlex

Abstract The COVID‐19 pandemic has put unprecedented strain on food supply chains. Given the ever‐increasing degree of globalization, those supply chains very often stretch across international borders. In the short run, countries have largely been working to keep those supply chains intact and operating efficiently so that panic buying is cooled and shifts in consumption habits arising from personal isolation can be accommodated. Once the crisis has passed, based on what has been learned regarding the international food system's resilience, governments may wish to strengthen institutions that govern international trade. On the other hand, based on their COVID‐19 experience, governments may feel that they are too dependent on foreign sources of supply and may wish to reverse the impacts of globalization on their food systems. As a result, they may become increasingly isolationist, eschewing international cooperation. Which of these opposing forces will prevail may depend on the paths economies follow after the disequilibrium precipitated by the pandemic.

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)0.001
metaresearch head score (Gemma)0.005
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.970
Threshold uncertainty score0.065

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.002
Scholarly communication0.0040.004
Open science0.0000.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0190.000

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 head0.078
GPT teacher head0.222
Teacher spread0.144 · 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

Citations188
Published2020
Admission routes2
Has abstractyes

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Same venueCanadian Journal of Agricultural Economics/Revue canadienne d agroeconomieSame topicCOVID-19 Pandemic ImpactsFrench-language works237,207