Building Resilience of Food Production in Calgary’s Community Gardens to an Increasing Number of Extreme Weather Events
Bibliographic record
Abstract
Extreme weather caused by climate change has reduced food security and adversely impacted ecosystems, including in Calgary’s network of 64 public community gardens. Increased instances of heat waves, drought, and hail have negatively impacted food production in Calgary’s community gardens. Developing the resilience of food grown in community gardens to extreme weather will safeguard a food source, which supplements gardeners’ diets and is donated to local charitable food organizations and ensure ongoing vibrancy of public space allocated to community gardens in Calgary. In this thesis, I ask how can food production in Calgary’s community gardens be resilient to increasing extreme weather events? In this study I interviewed four Community Garden Coordinators and five gardeners who were growing food in community gardens and distributed a questionnaire to all community gardens in Calgary, receiving 53 gardener responses. The interview and questionnaire questions gathered gardeners’ perspective relating to four research questions. How is food production in Calgary’s community gardens impacted by extreme weather events? How have individual gardeners in Calgary’s community gardens adapted food production strategies to extreme weather events? How have community gardens used cooperation to increase resilience of food production to extreme weather events? What garden design strategies were implemented to adapt food production to extreme weather? Themes identified in the responses were analyzed using the principles of social ecological resilience. This analysis indicated improvements are required in in Calgary’s community gardens to increases resilience of food production to extreme weather. This work highlights that regular meetings, communication, experimentation, and knowledge transfer are crucial to ensuring effective implementation of strategies to build resilience of food production to extreme weather. Increasing soil water retention, building microclimates, and planting adapted plant varieties are highlighted as effective strategies. This work fills a gap in knowledge by identifying strategies that build resilience of food production in Calgary’s community gardens to extreme weather.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.000 | 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".