The Skinny on Obesity: A need for a comprehensive diagnostic of obesity beyond self and health variables to include social and environmental strategic drivers
Bibliographic record
Abstract

 
 
 The rapid increase in the number of obese adults and children in both developed and developing countries is alarming and will strain health resources in the future. A review of pertinent social and built environmental influences that contribute to the prevalence of this chronic disease is examined with reference to current literature. This paper explores the relationship between factors of the built and social environments that ultimately lead to the creation of obesogenic environments. Recognizing the importance of human interaction, coupled with genetic factors, with the built environment in addressing obesity is an important variable, the author argues that rather than evaluating obesity with a retrospective approach, a forward thinking approach in creating built environments which entice human action in the environment should be an ongoing premise in fighting obesity.
 
 
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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 | 0.009 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.003 | 0.011 |
| Scholarly communication | 0.007 | 0.016 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.005 | 0.013 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".