Exploring collaboration within Edmonton's City Table on Household Food Insecurity during the COVID-19 pandemic
Bibliographic record
Abstract
During the COVID-19 pandemic, there has been unprecedented attention and funding toward addressing household food insecurity (HFI) in Canada. In Edmonton, a virtual "City Table" was developed to coordinate the myriad of HFI responses and begin to explore and address systemic issues underlying HFI. In this qualitative descriptive study, we asked: what are the opportunities for and challenges to collaboratively addressing HFI within Edmonton's City Table? In 2020, we conducted nine interviews with diverse professionals representing a local funding agency, the municipal food council, the City of Edmonton (community social work), the Edmonton Food Bank, the University of Alberta, ethno-cultural organizations, and other not-for-profit organizations supporting people experiencing poverty. Wenger's three modes of identification in a community of practice (CoP)—engagement, imagination, and alignment—were used to conceptually frame our qualitative analysis. Overall, we found that the HFI response sector reflects the beginnings of a CoP, but that inter-agency competition for funding and donations presents obstacles to the collaborative process. Findings highlight parallels between agencies and their clients, such as the mazes they must navigate to access resources. However, collaboration was facilitated by agencies' ideological cohesion and their shared struggle to address root causes of HFI. Analyses revealed some engagement amongst City Table members, but sparser imagination and alignment. A CoP does not yet exist because all three modes of identification are deficient in varying ways. Building engagement between agencies, shifting staff's imagination to a collective cause, and aligning practices are monumental tasks in this context.
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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 | 0.010 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.037 | 0.020 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".