Lessons from an evaluation of an urban Indigenous food sharing initiative in Southwestern Ontario: “I feel like I’m nourishing my spirit”
Bibliographic record
Abstract
OBJECTIVES: The aim of this study was to examine an urban Indigenous food sharing initiative through an evaluation attending to the Food Share Initiative's implementation and early outcomes. METHODS: This project used a community-based participatory research methodology to guide an evaluation of process and initial outcomes. Storytelling methods including interviews and a sharing circle, which took place in July and August 2021, were used to create a relational context for the research team and project participants, which honour Indigenous research methodologies. RESULTS: A total of 14 self-identifying Indigenous people participated in this evaluation. Initiative staff and Food Share recipients identified community relationships as a shared initiative experience that contributed to the wholistic health effects experienced by recipients. All participants recognized capacity limitations of both Food Share recipients and operational staff were important constraints to the initiative's process and implementation. Participant recommendations to improve the Food Share included enhanced outreach to involve other Indigenous community members as well as infrastructure like long-term funding and a central location to strengthen the initiative's operational capacity. CONCLUSION: As an important community food support, the Food Share's relational care approach fosters a meaningful and wholistic sense of nourishment for Indigenous community members in the Waterloo-Wellington Region.
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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 | 0.012 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.013 | 0.005 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.004 |
| 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".