Imagining Just and Sustainable Food Futures: Using Interactive Visualizations to Explore the Possible Land Uses and Food Systems Approaches in Revelstoke, Canada
Bibliographic record
Abstract
Food systems are linked to multiple critical sustainability issues such as climate change, environmental degradation, and growing socioeconomic inequalities, and there is a clear need for transformative changes in how food systems are imagined and enacted. For transformations to occur, local governments and stakeholders must be able to consider achievable and desirable futures that involve radically different reconfigurations of space and land use. Based in Revelstoke, Canada, this study uses interactive visualization methods to engage local government and food systems stakeholders in an exploration of three future food systems scenarios that center on changes in food supply, food affordability, and food governance. An interactive visualization tool was developed using the Unity3D game engine, which visualizes how transformations of an underutilized railway site in Revelstoke may appear in 2100. The visualizations were presented to the study participants (n = 10) through an online, Zoom-based workshop, where ‘walkthroughs’ of the scenarios were performed by the researchers and the participants subsequently provided feedback. The results of this study indicate that visualization tools can elicit emotional responses, convey human relationships with food and nature, communicate power dynamics, and incorporate social justice considerations. The results also show that the visualization’s representation of local infrastructure and services, the completeness of a virtual environment, and the plausibility of a depicted future affect the user assessment of the visualized scenarios.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".