Bibliographic record
Abstract
This dissertation is comprised of three essays, the goals of which are to provide an empirical understanding of how the income-health relationship evolves with child age and the underlying mechanisms. Previous research, conducted in US and Canadian settings, has found a positive association between household income and child health, which strengthens with age. One reason for this relationship may be that low-income children are more likely to suffer from chronic conditions than high-income children. While US research has controlled for the effects of parental health when examining the gradient, Canadian work has not. In Chapter 1, we seek to determine whether the Canadian findings persist after controlling for parental health status. Our results show that this adjustment reduces the size of the gradient in childhood and, importantly, indicates that it does not increase with age. In Chapter 2, we contribute to this literature by applying more flexible estimation techniques, namely nonparametric models, to understand the gradient in childhood. Our results provide evidence that our nonparametric model is closer to the true data generating process than the parametric model. Furthermore, our estimates confirm that the gradient does not increase with age, regardless of whether we control for parental health. In Chapter 3, we examine the relationship between family income, chronic conditions and child health. Generally, our results suggest that income does not have a significant impact on chronic conditions. Furthermore, we do not find the effect of chronic conditions on the probability of being in poor health differs by income levels, with the exception of asthma and mental handicap.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".