Impacts of the COVID-19 pandemic on food systems in Manitoba, Canada and ways forward for resilience: a scoping review
Bibliographic record
Abstract
Various studies over the past 3 years have synthesized trends and impacts of the COVID-19 pandemic on Canada’s national food system. However, less research has characterized the effects of COVID-19 within regional and provincial food system contexts. This article presents results from a scoping review of peer-reviewed and grey literature published from March 2020 until end of March 2023 examining the impacts of the COVID-19 pandemic on food systems in the province of Manitoba, Canada. Findings are presented according to the categories of food security funding, policy, and programming; individual and household food security; and food systems. In each area we synthesize key findings and discuss their significance in relation to existing food systems scholarship and national trends. Using review results, we propose priority areas for research and practice to support equitable and resilient food systems in Manitoba, including: (1) undertaking evaluation of food system policies, programs, and funding implemented during the pandemic (2) enhancing food security monitoring for vulnerable populations (3) further exploring community experiences and responses to food security, and (4) examining opportunities for local food systems development.
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 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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.010 | 0.022 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".