Bibliographic record
Abstract
In this paper, we propose a simple Watts-type measure of catastrophic health expenditure (CHE) which is an adaptation of the classic Watts poverty measure. The appeal of the proposed measure stems from the fact that it is both additively decomposable (i.e., it provides information on the contributions of the various population subgroups of interest, e.g., as categorized by gender, race, region, etc., to the overall level of CHE), and multiplicatively decomposable (i.e., it enables identification of three key drivers of CHE, namely, CHE incidence, CHE intensity and CHE inequality). We also describe how the Watts-type CHE measure can be estimated and additively decomposed using the widely available ordinary least squares regression packages. The empirical example provided shows the policy value of the Watts-type CHE measure, which makes it a useful supplement to the Foster-Greer-Thorbecke type measures of CHE recently proposed by Ogwang and Mwabu. Temporal dynamics in the Watts-type CHE measures are also introduced.
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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 | 0.002 | 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".