Exploring Obesogenic Food Environments in Edmonton, Canada: The Association between Socioeconomic Factors and Fast-Food Outlet Access
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it