Obesity in the built environment: a spatial analysis of two Canadian Metropolitan areas
Bibliographic record
Abstract
Global prevalence of obesity and overweight has rapidly increased over the past few decades.The relative growth rate of the epidemic, particularly in more developed countries, has triggered efforts to explore environmental determinants of weight gain.Research on how the built environment affects weight gain, and health more broadly, has been widely undertaken by public health, epidemiology, and geography disciplines, yet no clear relationships have been identified.Moreover, research on the Canadian context is generally lacking.Methods native to Geographic Information Systems (GIS) and spatial epidemiology may prove effective to furthering contemporary knowledge of the built environment determinants of obesity, and overall, contribute to wider disciplines involved.The first paper of this thesis reviews literature from the spatial epidemiology discipline to glean insight from recent methodological development of spatial clustering tools and provide guidelines for practical application.The second paper explores the spatial clustering of obesity and examines the built environment for potential correlates.Both papers take a unique perspective within the respected disciplines they are informing, and thus provide novel results for future research and development.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.018 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".