Neighbourhood food environments revisited: When food deserts meet food swamps
Bibliographic record
Abstract
This study uses service area–based coverage and various count regression methods to assess neighbourhood‐level healthy and unhealthy food environments, and food access associated with different socio‐economic groups in Edmonton, Canada. We identify three types of vulnerable neighbourhoods according to different food environments: food deserts (i.e., neighbourhoods lack sufficient access to healthy foods); food swamps (i.e., neighbourhoods have excess access to unhealthy foods); and those with overlaps of food swamps and food deserts. We also identify neighbourhoods with superior access to healthy foods (i.e., food oases). Additionally, our results from regression analyses indicate: (1) child population is negatively associated with both healthy and unhealthy food resources; (2) good access to public transportation is associated with good coverage of all healthy food outlets and convenience stores; and (3) deprived neighbourhoods with higher percentages of minority populations have better coverage of both healthy and unhealthy foods in general. The results from this study can help the City of Edmonton identify the key neighbourhoods with high potential for local business and the hotspot neighbourhoods that require particular support. Tailored strategies are proposed to effectively and efficiently improve food environments with limited resources.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".