“I Haven’t Had Moose Meat in a Long Time”: Exploring Urban Indigenous Perspectives on Traditional Foods in Saskatchewan
Bibliographic record
Abstract
This qualitative study investigates the perspectives of urban Indigenous individuals in Saskatchewan, Canada, regarding their consumption of traditional foods. Through in-depth, semi-structured interviews with 14 participants across Saskatoon, Regina, and Prince Albert, the research aimed to uncover the benefits, risks, and barriers associated with acquiring and consuming traditional foods. Participants emphasized the nutritional advantages of traditional foods, such as higher nutrient density and absence of industrial additives, which they linked to improved health outcomes and alignment with Indigenous biology. The study also highlighted the vital role of traditional foods in maintaining cultural identity and fostering community connections through practices of food sharing and intergenerational knowledge transfer. However, significant challenges were identified, including economic and physical barriers to access, environmental degradation, and regulatory issues that restrict the availability of traditional foods in urban settings. The findings suggest a complex landscape where cultural practices are both preserved and challenged within the urban environment. This study contributes to the broader understanding of how Indigenous populations navigate the preservation of their culinary heritage in the face of modern economic and environmental pressures, providing insights for policy and community-based interventions aimed at supporting Indigenous food sovereignty.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.016 | 0.012 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".