Retail food environments, shopping experiences, First Nations and the provincial Norths
Bibliographic record
Abstract
INTRODUCTION: This paper looks at the market food environments of First Nations communities located in the provincial Norths by examining the potential retail competition faced by the North West Company (NWC) and by reporting on the grocery shopping experiences of people living in northern Canada. METHODS: We employed two methodological approaches to assess northern retail food environments. First, we mapped food retailers in the North to examine the breadth of retail competition in the provincial Norths, focussing specifically on those communities without year-round road access. Second, we surveyed people living in communities in northern Canada about their retail and shopping experiences. RESULTS: Fifty-four percent of communities in the provincial Norths and Far North without year-round road access did not have a grocery store that competed with the NWC. The provinces with the highest percentage of northern communities without retail competition were Ontario (87%), Saskatchewan (83%) and Manitoba (72%). Respondents to the survey (n = 92) expressed concern about their shopping experiences in three main areas: the cost of food, food quality and freshness, and availability of specific foods. CONCLUSION: There is limited retail competition in the provincial Norths. In Manitoba, Saskatchewan and Ontario, the NWC has no store competition in at least 70% of northern communities. Consumers living in northern Canada find it difficult to afford nutritious foods and would like access to a wider selection of perishable foods in good condition.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".