Exploring Obesogenic Food Environments in Edmonton, Canada: The Association between Socioeconomic Factors and Fast-Food Outlet Access
Bibliographic record
Abstract
PURPOSE: To explore the relationship between the placement of fast-food outlets and neighborhood-level socioeconomic variables by determining if indicators of lower socioeconomic status were predictive of exposure to fast food. DESIGN: A descriptive analysis of the fast-food environment in a Canadian urban center, using secondary analysis of census data and Geographic Information Systems technology. SETTING: Edmonton, Alberta, Canada. MEASURES: Neighborhoods were classified as High, Medium, or Low Access based on the number of fast-food opportunities available to them. Neighborhood-level socioeconomic data (income, education, employment, immigration status, and housing tenure) from the 2001 Statistics Canada federal census were obtained. ANALYSIS: A discriminant function analysis was used to determine if any association existed between neighborhood demographic characteristics and accessibility of fast-food outlets. RESULTS: Significant differences were found between the three levels of fast-food accessibility across the socioeconomic variables, with successively greater percentages of unemployment, low income, and renters in neighborhoods with increasingly greater access to fast-food restaurants. A high score on several of these variables was predictive of greater access to fast-food restaurants. CONCLUSION: Although a causal inference is not possible, these results suggest that the distribution of fast-food outlets relative to neighborhood-level socioeconomic status requires further attention in the process of explaining the increased rates of obesity observed in relatively deprived populations.
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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 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".