Bibliographic record
Abstract
CONTEXT: In a recent article in this journal, Sam Harper and his colleagues (2010) call for increased awareness and open dialogue of moral judgments underlying health inequality measures. They recommend that analysts use relative inequality measures when concerned only about health inequality but use absolute inequality measures when also concerned about other issues, such as the overall level of population health and the level of health for each group in the population. METHODS: Using a simple, hypothetical example, this commentary shows that the relationships among inequality, the absolute level for each group, and the overall level in the population are more complex than suggested by the analysis by Harper and his colleagues. FINDINGS: First, analysts must make the choice of absolute or relative inequality measures, separately, for single- and multiple-population cases. Second, in the single-population cases, analysts can use both relative and absolute inequality measures when concerned only about health inequality independent of other considerations. Third, in almost all real-world multiple-population cases, when using either the absolute or relative inequality measure, the assessment of health inequality is influenced by the absolute level of health for each group. CONCLUSIONS: The choice between absolute and relative inequality measures is not about the independent normative significance of inequality, as Harper and his colleagues suggest. In choosing between absolute and relative measures, future work needs to integrate an empirical examination of values, a moral assessment of values, and a technical understanding of inequality measures.
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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 | 0.062 | 0.153 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.040 |
| Scholarly communication | 0.010 | 0.015 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.039 | 0.054 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".