Participatory Research Methods for Examining Lessons from COVID-19 about Local Food Systems Vulnerabilities to Exogenous Shocks
Bibliographic record
Abstract
Participatory processes are integral to sustainability and resilience planning; involving diverse stakeholders ensures planning practices and outcomes are grounded in local social, economic, environmental, and cultural contexts and realities. It follows that research and tools supporting planning processes should also be participatory, and such research approaches can lead to useful knowledge for developing appropriate, place-based approaches for addressing critical sustainability issues. Using the Fraser Valley region (British Columbia, Canada) as a case study, this research experiments with participatory research (PR) methods and tools for supporting long-term food systems planning by examining regional food vulnerabilities and opportunities/needs for building resilience to exogenous shocks. The research involved a survey and a series of workshops supported by an online collaboration platform, CoLabS, which engaged different food system stakeholders to first, reflect on what COVID-19 has revealed about regional food systems vulnerabilities, and second, discuss how these insights can be used for integrated long-term planning and increasing food resilience in the face of a variety of environmental and socioeconomic hazards. Strengths of this research include its place-based approach, relationship development and reciprocity aspects, multi-dimensional exploration of vulnerabilities and issues, and the use of dynamic digital tools. Limitations of the research include its lack of comprehensive participation and representation, capacity limitations of potential participants, influence of current real-world issues on research activities, and limited functionality of some online tools. Lessons and insights from this research demonstrate the importance of employing adaptable and flexible methods and tools when conducting PR on sustainability issues.
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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 | 0.172 | 0.112 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.010 | 0.014 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.003 | 0.005 |
| 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".