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 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 | 0.017 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".