Food security and Canada's agricultural system challenged by COVID‐19: One year later
Bibliographic record
Abstract
Abstract This paper assesses the earlier projections made by the authors in March 2020 about the impact of COVID‐19 on Canada's food security. First, as measured in the early part of the second quarter of 2020, COVID‐19 is associated with an increased prevalence of household food insecurity as measured by Statistics Canada. Also, as we predicted, we did not observe a rapid general increase in food prices that would have suggested a breakdown in parts of the food system. In this regard, we now develop a general insight that we believe is worthy of ongoing consideration. Put simply, concerns expressed about food insecurity should not be seen as tantamount to a failure of our food supply system. Household income, for example, is an important part of the story. The converse is also true: the success of our food supply system as measured by its capacity to adapt to challenges like COVID‐19 or provide a variety of food at relatively low prices—while necessary, and (in our opinion) critical considerations—will not alone eliminate food insecurity in Canada. The oversimplified conflation of food insecurity concerns with the robustness of our food supply system does a disservice to ongoing efforts to address food insecurity as well as our capacity to assess and improve the Canadian food supply system.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.003 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".