Vertical agriculture’s potential implications for food system resilience: outcomes of focus groups in the Fraser Valley, British Columbia
Bibliographic record
Abstract
Food systems are vulnerable to socioeconomic and environmental changes, and increasing food system resilience has emerged as a key public policy priority. Vertical agriculture is one potential strategy to contribute to food system resilience. To date, the literature exploring this technology has focused on consumer perceptions, with less examination of broader social-ecological system considerations or on-the-ground implications for vulnerable food system actors. This study explores local food system stakeholders’ account of vertical agriculture’s potential contributions to food system resilience, and specifically to food system decentralization, increasing stakeholder decision-making autonomy and diversifying food systems. The results of an exploratory focus group with 16 participants, including local government officials and members of community food organizations, are presented. Participants discussed how vertical agriculture can contribute to food system decentralization by operating across a variety of scales and in public institutions, although local land-use zoning barriers, competing land use priorities, and lack of community acceptance may hinder its widespread implementation. Further, vertical agriculture may contribute to food system diversification by increasing the amount of regionally available produce in the face of future supply disruptions. The potential implications of vertical agriculture for increasing decision-making autonomy will vary, depending on whether it is governed at more or less accessible scales and forms. It is recommended that further research consider diverse scenarios and engage with wider stakeholder and community groups for vertical agriculture’s planning and implementation, particularly in less explored land use contexts, such as in agricultural regions or in public institutions.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".