Obesity, Economic Growth: The COVID-19 Pandemic, and Poverty
Bibliographic record
Abstract
Over the past several decades, obesity has grown into a major global epidemic. This dramatic rise in rates is more pronounced in developing countries, as compared to more industrialized societies. In Peru, obesity rates are escalating. In this paper, Granger Causality Test in Panel Data, Dynamic Panel Data Analysis, and Cointegration Analysis in Panel Data from 2008 – 2020 in 24 regions with a sample size of 312 data points – is used. We find that there is a cointegration among obesity, poverty, and economic growth, which ensures a long-run relationship between the variables. In addition, our analysis found that regarding the initiative of government regulation, which introduces technical parameters of processed foods and nonalcoholic beverages and which was approved in terms of sugar, sodium and saturated fat content, is not effective. Finally, the results obtained show that the quarantine period in the context of the COVID-19 pandemic also has contributed to an increase in obesity rates in the regions of Peru during the 2008 – 2020 period.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".