Participatory System Mapping for Food Systems: Lessons Learned from a Case Study of Comox Valley, Canada
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Food systems are complex and multifaceted, comprising a diverse range of actors, processes, and interactions. Participatory system mapping can be employed to help understand this complexity and support the development of sustainable and resilient food systems. This article shares a participatory mapping approach that has been developed as part of the Climate–Biodiversity–Health (CBH) Nexus project in the Comox Valley, British Columbia, Canada. This research pursues two main aims: (1) to ground truth in the CBH system map of food systems, developed with the participation of stakeholders; and (2) to explain how participatory system mapping can be employed to clarify the complexity of food systems in a clear and concise manner for all stakeholders. This research contributes to the literature on participatory system mapping, including critiques of its practical utility, by employing participatory approaches to visualize multi-dimensional and multi-level system maps with an emphasis on verifying that they are clear, understandable/useful, and reliable for diverse stakeholder audiences.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it