Poverty and the extent of child obesity in Canada, Norway and the United States
Bibliographic record
Abstract
The goal of this paper is to compare the extent of child obesity in Canada, Norway and the United States. As child poverty is an important correlate of child obesity, we wish to examine the potential role of international differences in child poverty in explaining international differences in the extent of child obesity. We use three representative microdata surveys containing parental reports of child height and weight collected in the mid-1990s in Canada, Norway and the US. We calculate both the prevalence and proportional severity of child obesity for 6-11-year-old children in each country, and represent the 'extent' of obesity diagrammatically. Differences in patterns of child poverty are similarly depicted. Obesity extent is also compared for poor and non-poor children in Canada and the US. Finally, child obesity in the three countries is compared using only non-poor children where we find that the extent of child obesity is much lower in Norway than in Canada or the US. The pattern apparent for obesity is remarkably similar to that found for child poverty. In Canada and especially in the US, we find a much greater extent of obesity for poor than non-poor children. However, when we compare only non-poor children in the three countries, although the magnitude of difference is smaller, it remains clear that Norwegian children are much less likely to be obese. Policy and research directed towards reducing the extent of child obesity in both Canada and the US should pay particular attention to issues of child poverty.
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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 | 0.003 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".