Building capacity through urban agriculture: report on the askîy project
Bibliographic record
Abstract
INTRODUCTION: Many North American cities have a built environment that provides access to energy-dense food and little opportunity for active living. Urban agriculture contributes to a positive environment involving food plant cultivation that includes processing, storing, distributing and composting. It is a means to increase local food production and thereby improve community health. The purpose of this study was to understand how participating in urban agriculture can help to empower young adults and build capacity for growing food in the city. METHODS: This was a qualitative study of seven participants (five Indigenous and two non-Indigenous) between the ages of 19 and 29 years, engaged as interns in an urban agriculture project known as "askîy" in Saskatoon, Saskatchewan, Canada in 2015. We used a case-study design and qualitative analysis to describe the participants' experience based on the sustainable livelihoods framework. RESULTS: A collaborative approach had a great effect on the interns' experiences, notably the connections formed as they planned, planted, tended, harvested and sold the produce. Some of the interns changed their grocery shopping habits and began purchasing more vegetables and questioning where and how the vegetables were produced. All interns were eager to continue gardening next season, and some were planning to take their knowledge and skills back to their home reserves. CONCLUSION: Urban agriculture programs build capacity by providing skills beyond growing food. Such programs can increase local food production and improve food literacy skills, social relationships, physical activity and pride in community settings.
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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".