The impact of COVID‐19 on agricultural market integration in Eastern Canada
Bibliographic record
Abstract
Since the outbreak of the coronavirus pandemic in early 2020 and the resulting economic fallout, reports and official statistics have pointed to an unequivocal effect of the disease on almost all global economic activities, including the agricultural and agri-food sectors. The aim of this article is to use a price transmission approach in order to study the price relationships of agricultural commodities, including potatoes, corn, hogs, eggs, and chicken between regional Canadian markets and to verify their economic integration. The method of panel cointegration is applied to investigate the potential impact of the pandemic on the spatial integration of the provincial agricultural markets in Eastern Canada. It is found that these markets were fully integrated and efficient prior to COVID-19 restrictions. However, the statistical results show that travel restrictions and labor shortages represented trade barriers between the provinces, and they are likely the factors that impacted the price transmission mechanism, and consequently the markets became much less integrated. It is suggested that government policies should include actions that would manage future shocks to the agricultural commodity prices by accelerating the necessary transformations in the agri-food sector to make it more resilient and less vulnerable to future pandemics and other potential natural challenges.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".