Retail store governance models in remote Indigenous communities across Canada: a media analysis
Bibliographic record
Abstract
In remote Indigenous communities across Canada, food insecurity is shaped by systemic inequities rooted in colonial governance structures and compounded by geographic isolation, high operational costs, limited retail competition, and small population sizes. While these material challenges are well-documented, less attention has been given to the governance of retail food systems and the ownership models that mediate access, affordability, and community control. In contrast to Australia, where Indigenous retail governance has received growing academic focus, Canadian research in this area remains sparse—despite growing public discourse and recurring media coverage. Here, we survey news articles to (1) Describe retail food governance structures and their impacts (particularly on: ownership models, community engagement, food affordability, and food accessibility) in remote Indigenous communities, focusing on community perspectives, and (2) Develop a Framework of Retail Food Models in Remote Indigenous Communities in Canada. We conducted a systematic media analysis of news articles extracted from the Canadian Major Dailies database (via search string; n = 148) and Google News search (using 10 searches × 4 pages/10 results; n = 400). Using a double screening process, we applied structured inclusion and exclusion criteria to select articles that addressed the current governance structures of retail stores in remote, Indigenous communities across Canada. We used a hybrid coding approach based on four main themes: (1) store ownership models; (2) community engagement; (3) food affordability; and (4) food accessibility. We purposefully extracted direct quotes from the news articles to retain community perspectives and minimize journalistic bias. Of the 70 articles, 16 discussed store ownership models, 14 discussed community engagement, 22 discussed food affordability, and 18 discussed food accessibility. Findings were used to develop a Framework of Retail Food Models in Remote Indigenous Communities in Canada. There is growing interest in Indigenous-owned grocery stores and co-ops across Canada. Many communities use Indigenous-led governance initiatives to strengthen local economies through retail food systems. While these initiatives are not widely documented in academic literature, news media offers an important source for new insights.
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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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.015 | 0.026 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.003 |
| 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".