A robust composite indicator framework for evaluating Sustainable Development Goal 3 using benefit‐of‐the‐doubt models and principal component analysis
Bibliographic record
Abstract
Abstract This study proposes a novel benefit‐of‐the‐doubt (BOD) model for constructing composite indicators (CIs) to assess Sustainable Development Goal 3 across the World Health Organization (WHO) member states. To address the BOD model's limited ability to differentiate among states with many sub‐indicators, we introduce a common weight BOD (CWBOD) model to improve cross‐country comparability. To improve the model's discriminatory power, we apply principal component analysis (PCA) to reduce the number of sub‐indicators, using the resulting principal components as inputs to the BOD model. To account for the uncertainty due to possible information loss in PCA, we further develop a robust BOD (RBOD) model. The final CI scores are computed using the geometric mean of the BOD, CWBOD, and RBOD scores. We apply this integrated framework to compute a Public Health Index for 177 WHO member states, enabling a more precise and robust evaluation of global public health performance.
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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 | 0.008 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".