Bibliographic record
Abstract
The paper considers the measurement of health opportunity with categorical data of health status. A society's health opportunity is represented by an income-health matrix that relates socioeconomic class with health status; each row of the matrix corresponds to a socioeconomic class and contains the respective probability distribution of health. In the first part of the paper, we formally demonstrate an important limitation in applying standard inequality criteria to distributions of health: without specifying the cardinal value for each health status, it is impossible to em- ploy Lorenz dominance in measuring health inequality. In the second part of the paper, we argue that it is a more sensible approach to measure in- equality of health opportunity. By introducing a monotone assumption on the income-health matrix, we derive a sequence of welfare-dominance con- ditions for health-opportunity comparisons. We then obtain dominance conditions for Lorenz curve-based inequality-rankings of health opportu- nities. Finally, we apply the results to compare health opportunities in the US and Canada using the newly released JCUSH data.
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How this classification was reachedexpand
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 | 0.005 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".