The Retail Food Sector and Indigenous Peoples in High-Income Countries: A Systematic Scoping Review
Bibliographic record
Abstract
Indigenous Peoples in high-income countries experience higher burdens of food insecurity, obesity, and diet-related health conditions compared to national averages. The objective of this systematic scoping review is to synthesize information from the published literature on the methods/approaches, findings, and scope for research and interventions on the retail food sector servicing Indigenous Peoples in high-income countries. A structured literature search in two major international databases yielded 139 relevant peer-reviewed articles from nine countries. Most research was conducted in Oceania and North America, and in rural and remote regions. Several convergent issues were identified across global regions including limited grocery store availability/access, heightened exposure to unhealthy food environments, inadequate market food supplies (i.e., high prices, limited availability, and poor quality), and common underlying structural factors including socio-economic inequality and colonialism. A list of actions that can modify the nature and structure of retailing systems to enhance the availability, accessibility, and quality of healthful foods is identified. While continuing to (re)align research with community priorities, international collaboration may foster enhanced opportunities to strengthen the evidence base for policy and practice and contribute to the amelioration of diet quality and health at the population level.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 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".