Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Although a growing body of research has examined the association between food prices and the availability of fast food restaurants on weight outcomes, there is limited empirical evidence on the direct effect of eating behavior on body weight. OBJECTIVE: The effect of eating behavior on obesity prevalence among Canadians is examined. METHODS: A nationally representative sample from the Canadian National Population Health Survey (2000-2008) with 29 722 observations is used. Obesity prevalence is estimated by a linear probability model using cross-sectional and panel estimation methods. Separate regressions are estimated for males and females. RESULTS: Multivariate analyses suggest that eating behavior has a statistically significant effect on obesity prevalence. In particular, individuals who reported excellent, very good, and good eating behavior have a lower risk of obesity compared with those with fair or poor eating behavior. Although cross-sectional and panel data methods produce consistent results, the cross-sectional model overestimates the effect of eating behavior on the risk of obesity. This highlights the importance of controlling for unobserved individual factors that may affect how eating behavior is related to body weight. CONCLUSION: Evidence is found showing that eating behavior is an important determinant of obesity prevalence. The findings suggest that improving the eating behavior of individuals would help reduce excessive body weight and its induced health risks.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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