Growing in relation with the land: Experiential learning of Root and Regenerate Urban Farms
Bibliographic record
Abstract
The food landscape of Calgary, Canada, is sown with an abundance of polycultures. Alongside place-specific Indigenous foodways are food rescue, banking, and hamper programs, food studies scholars, a City of Calgary food resilience plan, and a growing number of alternative food network producers. Within the local alternative food network, there has been a boom in advancing indoor growing for our colder climate, including container, aquaponic, vertical hydroponic, and greenhouse growing. Situated as an agrarian ethnographer and an urban regenerative farmer, we seek to highlight the viability of agricultural techniques that are in relation with the land to grow more socially and ecologically sustainable food and farm systems in and around Calgary. From this position, we formed a collaboration between the University of Calgary, Root and Regenerate Urban Farms, and the Young Agrarians to document the cultivation process for a production urban farm. Over the course of one growing season—May to September, 2021—we harvested approximately 7,000 lbs (3,175 kg) of produce across nine urban spaces totaling 0.26 acres. The 48 vegetable varieties were distributed to 35 community supported agriculture shareholders, weekly farmers market customers, restaurant chefs, and members of the YYC Growers and Distributors cooperative. Moreover, we donated 765 lbs (347 kg) of surplus produce to the Calgary Community Fridge, Calgary Food Bank, and the Alex Community Food Centre, which work to mitigate food insecurity. Through a reflexive practitioner approach, our reflective essay discusses the benefits and limitations of Small Plot Intensive Farming methods and urban land-sharing strategies, as well as the viability of land-based urban agriculture in a rapidly changing socio-ecological climate. Our paper also demonstrates the potential for transcending siloed approaches to knowledge-making vis-à-vis experiential learning partnerships between graduate student researchers, farmers, and agricultural organizations.
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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 | 0.001 | 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 | 0.001 |
| 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".