Food System Scenarios in Uncertain Futures: A Case Study on Long-Term Local Food System Planning in Revelstoke, Canada
Bibliographic record
Abstract
Scenario planning is a potentially effective method for supporting long-term planning for sustainable and resilient food systems; however, scenario exercises are often limited by too much focus on a single preferred future, not accounting for uncertainty in global trajectories and future conditions. This study engaged local food system actors in Revelstoke (Canada) in a workshop that explored a qualitative, scenario-based approach to long-term food systems planning in the face of uncertain futures. The study involved applying different global narratives to identify future local scenario alternatives that respond to the socioeconomic, environmental, and political pressures in these narratives. This study identifies two trajectories and sets of possible future conditions (i.e., Scenario 1 and Scenario 2) that differ from one another in the following areas: (1) health and wellbeing, (2) connectivity and scale, (3) human–environment interactions, and (4) economies and the nature of work. Additionally, the strengths and weaknesses of the qualitative scenario method developed and used in this study were identified, including considerations related to the application of the method, participant selection, the nature of the data, and the assessment (or lack thereof) of the likelihoods of future events. The insights from such a scenario-planning approach can be used to stimulate thinking about what actions and interventions are useful for making progress toward local wellbeing, sustainability, and resilience in the face of global challenges and exogenous shocks.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".