The self-reinforcing dynamics of economic insecurity and obesity
Bibliographic record
Abstract
This article models the dynamic effects of economic insecurity on body weight. Using Australian panel data, we infer an individual’s level of economic insecurity as a function of exposure to various financial risks and employ regression equations to explore its effect upon current period body mass index (BMI) scores. Estimates reveal that a sustained standard deviation increase in economic insecurity raises an individual’s BMI at a rate of approximately 0.35 units per year. Quantile regressions are then used to estimate the sensitivity of body weight to insecurity at different percentiles of the distribution and we find that persons who are overweight and obese are much more seriously affected. This implies that shocks that make individuals more financially vulnerable can generate harmful self-sustaining cycles of risk and weight gain. We also model the dynamics of insecurity and show that it is a persistent phenomenon for persons with high levels of exposure and lower incomes. This finding indicates that persons of lower socio-economic status are more likely to encounter vicious cycles of increasing insecurity and obesity, which partially explains why weight-related health problems are unusually highly concentrated amongst these individuals.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".