THEY FEED ME GOOD Relational Food Systems in Saskatoon
Bibliographic record
Abstract
This research study examines the foodways of Saskatoon households, exploring relational food networks as a factor toward fairer health outcomes with a focus on resistance, resilience, and culture. This study uses critical ethnography to glean an accounting for trauma and an accounting for uplifting relational food networks. Data are drawn from interviews, photographs, media (animations), and participant observation. An iterative analysis is informed by intersectional and relational frameworks and follows a hybrid inductive-deductive approach. Findings are presented in representational and creative ways, with participants sharing stories that unveil the problematic of resilience in the face of colonialism and its relationship to food systems and health. The discussion considers socio-cultural factors, systemic racism, and inequality to advance a better understanding of cultural dimensions and political constraints linked to food insecurity. It contributes an accounting of variation in urban households in Saskatoon, their food choices, and their foodways, including models of governance that mitigate system failures to keep families fed. The lives of Saskatoon people in this study come together with separate stories of healing and violence, power and cultural restitutions of health, joy, and food. Participants live in different households but share similar collective histories of colonization and relentless systemic disparity. Their stories are also connected through the negotiation of food related wellbeing in urban spaces that re-dignify connections to culture, restore relational food strategies, and reclaim the land.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".