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Record W3021395578 · doi:10.1111/cjag.12232

Risk management in Canada's agricultural sector in light of COVID‐19

2020· article· en· W3021395578 on OpenAlex
Alan P. Ker

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
venuePublished in a venue whose home country is Canada.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueCanadian Journal of Agricultural Economics/Revue canadienne d agroeconomie · 2020
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgricultural risk and resilience
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsAgricultureGovernment (linguistics)AgribusinessPandemicBusinessCoronavirus disease 2019 (COVID-19)Risk managementEconomic growthPolitical scienceEconomic policyEconomicsFinanceGeography

Abstract

fetched live from OpenAlex

Abstract The unexpected introduction and spread of COVID‐19 has presented significant challenges for every aspect of Canadian society. Although the food and agricultural sector is positioned better than most, there are many risks that will need to be managed in the coming months. The suite of Federal‐Provincial‐Territorial Business Risk Management (BRM) programs delivered under the Canadian Agricultural Policy framework are meant to assist farmers in managing risks; however, there are no corresponding specialized programs for agribusinesses. The underlying structure of the BRM program was developed decades ago and certainly not with any thought to the possibility of a global pandemic. This article considers to what extent the BRM program and, more broadly, government programming will assist farmers in managing new risks. By default, the article is speculative in nature given that we are currently at the onset of the pandemic in Canada.

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.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.114
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.018
GPT teacher head0.154
Teacher spread0.136 · 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