The importance of food retail stores in identifying food deserts in urban settings
Bibliographic record
Abstract
While food deserts in urban places have been fairly well studied in North America and Europe, there is little consensus on the best conceptual and operational definition for food deserts.In most of these studies researchers concentrate on mainstream grocery stores and supermarkets as the only sources of healthy and affordable food options especially in cities with diverse ethnic population.The purpose of this study is to expand this usual approach to food desert studies by investigating the inclusion of ethnic food stores and specialty stores as sources of healthy food options in a multi-ethinic Toronto neighbourhood.The Englemount-Lawrence neighbourhood was selected for this study as it has been identified as a food desert in previous studies in Toronto.An in-store survey was conducted in order to identify ethnic and specialty stores which supply healthy and affordable food options based on US Department of Agriculture dietary guidelines.Using Geographic Information System (GIS) analysis, all qualified ethnic food stores in the study area were geocoded into a neighbourhood map and a buffer of 1000m was drawn around each.We found out that ethnic food stores supplying healthy and culturally-accepted food options are evenly dispersed across the Englemount-Lawrence neighbourhood.We conclude that, unlike in previous studies, this neighbourhood is not a food desert.Furthermore, failure to use an inclusive set of healthy food stores and culturally acceptable food choices, in neighbourhood studies of food deserts can significantly alter the results in the study area and hence mislead food planners and policymakers in decision-making.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".