Growing With Lady Flower Gardens: Governance in a Land-based Initiative Focused on Building Community, Well-being and Social Equity Through Food
Bibliographic record
Abstract
The local food sector has been gaining strong momentum in the province of Alberta but inclusiveness, social equity, and affordability remain issues of concern. Lady Flower Gardens (LFG) is a community-based initiative that is working to address these issues. Established in 2012 on private land in the northeast edge of Edmonton, Alberta, LFG provides opportunities for marginalized and disadvantaged individuals to develop skills in growing food for their own consumption, contribute a share of the harvest to the Edmonton Food Bank, and develop relationships and build community in a healthy and safe environment. LFG collaborates with a number of social service agencies and two universities in the development of this land-based, experiential learning model. In this case study we examine LFG’s evolving governance structure, from a small informal grassroots initiative to a self-governed Part 9 non-profit company, registered with the provincial government. We gathered data from in-depth semi-structured interviews as well through site visits, participant observation and documentary research. Our analysis uses a food justice lens and the Policy Arrangement Approach as adapted by Van der Jagt et al. (2017) to examine LFG’s actors, partnerships and participation, resources, discourse, and rules. Investigating these dimensions of LFG provides insights into the complexity of factors, both internal and external, that have influenced the development and governance of this local food initiative and its ability to contribute to inclusiveness, social equity, and food justice. Our research reveals that LFG aligns strongly with FLEdGE’s good food principles of food access and ecological resilience, while also intersecting with the principle of farmer livelihoods through the creation of new training opportunities.
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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 | 0.002 | 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 | 0.011 | 0.015 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.005 |
| 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".