Reflecting on COVID-19 for integrated perspectives on local and regional food systems vulnerabilities
Bibliographic record
Abstract
The COVID-19 pandemic has highlighted multiple vulnerabilities and issues around local and regional food systems, presenting valuable opportunities to reflect on these issues and lessons on how to increase local/regional resilience. Using the Fraser Valley Regional District (FVRD) in Canada as a case study, this research employs integrated planning perspectives, incorporating comprehensive-systems, regional, place-based, and temporal considerations, to (1) reflect upon the challenges and vulnerabilities that COVID-19 has revealed about local and regional food systems, and (2) examine what these reflections and insights illustrate with respect to the needs for and gaps in local/regional resilience against future exogenous shocks. The study used a community-based participatory approach to engage local and regional government, stakeholders, and community members living and working in the FVRD. Methods consisted of a series of online workshops, where participants identified impacts related to the food production, processing, distribution, access, and/or governance response components of the local and regional food systems and whether these impacts were short-term (under 3 months), medium-term (3 to 12 months), or long-term (over 1 year) in nature. Findings from the study revealed that food systems and their vulnerabilities are complex, including changes in food access and preparation behaviours, lack of flexibility in institutional policies for making use of local food supply, cascading effects due to stresses on social and public sector services, and inequities with respect to both food security impacts and strategies/services for addressing these impacts. Outcomes from this research demonstrate how including comprehensive-systems, regional, place-based, and temporal considerations in studies on food systems vulnerabilities can generate useful insights for local and regional resiliency planning.
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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 | 0.015 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.025 | 0.018 |
| Scholarly communication | 0.016 | 0.009 |
| Open science | 0.004 | 0.017 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.018 | 0.002 |
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".