Using a Community of Practice Approach to Respond to Food Insecurity During the COVID-19 Pandemic in Edmonton, Alberta
Bibliographic record
Abstract
Hunger and food insecurity have a long history and prevalence, with the oldest food bank in Canada and hundreds of community agencies responding to food insecurity. This research began in partnership with the Community University Partnership (CUP) at The University of Alberta to support network building in this sector. As the COVID-19 pandemic emerged, food insecurity increased and all levels of government responded with increased availability of funding for responding to food insecurity. This funding also allowed for new organizations to enter the food insecurity response sector in Edmonton. The City of Edmonton then responded to this change in the sector by hosting a table on the collaboration and coordination of food insecurity responses, involving several community agencies. The focus of this research shifted in partnership with what this research calls “The City Table” to support their network and community building process. This research asks: how can the experiences of community agencies, donors and funders inform the building of a collaborative response to food insecurity during crises? Qualitative interviews were used to gain a depth of understanding in this sector, which was then supplemented by the insights gained through participation at The City Table to create an iterative community based research process. Elven interviews were conducted with professionals representing community agencies, donors of food and funders, and were selected based on the depth and richness of their anticipated insights, as informed by the research’s active involvement with this sector in a “snowball” approach. Drawing from the literature on the formation of communities of practice, the themes of engagement, imagination and alignment were used to guide the analysis of the data collected. The research found that this sector has the beginnings of forming a community of practice as a learning community that may support collaboration on responses to food insecurity. However, competition between agencies for funding and donations, as well as unstandardized data collection in the sector were identified as obstacles to the community of practice process. Further research is recommended in bridging the learnings generated by other poverty response sectors in Edmonton, particularly the housing insecurity sector, to gain insights into supporting the community of practice process.
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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 | 0.027 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.030 | 0.014 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.007 | 0.016 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".