Additional file 1 of Spatiotemporal analysis of the effect of global development indicators on child mortality
Bibliographic record
Abstract
Additional file 1: Table S1. Summary information of countries included in this study. Table S2. Proportion of missingness and number of data points (n=3,510). Table S3. Diagnostics information for space-time interaction model selection. Figure S1. Spatial varying coefficients for the effects of development indicators on under-five mortality rates in the year 2000 showing countries with significant observed effects (95% Credible Interval). EDU (Secondary education, pupils (% female)); EMP (Employment to population ratio, 15+, female (%) (ILO estimate)); GDP (Gross domestic product—per capita (current US$)); GNE (Gross national expenditure (% of GDP)); OHP (Out-of-pocket expenditure (% of current health expenditure)); HEXP (Current health expenditure (% of GDP)); WATER (People using at least basic drinking water services (% of population)); DEF (People practicing open defecation (% of population)); SANI (People using at least basic sanitation services (% of population)); PM (PM2.5 air pollution, mean annual exposure (micrograms per cubic meter)). Figure S2. Spatial varying coefficients for the effects of development indicators on under-five mortality rates in the year 2005 showing countries with significant observed effects (95% Credible Interval). EDU (Secondary education, pupils (% female)); EMP (Employment to population ratio, 15+, female (%) (ILO estimate)); GDP (Gross domestic product—per capita (current US$)); GNE (Gross national expenditure (% of GDP)); OHP (Out-of-pocket expenditure (% of current health expenditure)); HEXP (Current health expenditure (% of GDP)); WATER (People using at least basic drinking water services (% of population)); DEF (People practicing open defecation (% of population)); SANI (People using at least basic sanitation services (% of population)); PM (PM2.5 air pollution, mean annual exposure (micrograms per cubic meter)). Figure S3. Spatial varying coefficients for the effects of development indicators on under-five mortality rates in the year 2010 showing countries with significant observed effects (95% Credible Interval). EDU (Secondary education, pupils (% female)); EMP (Employment to population ratio, 15+, female (%) (ILO estimate)); GDP (Gross domestic product—per capita (current US$)); GNE (Gross national expenditure (% of GDP)); OHP (Out-of-pocket expenditure (% of current health expenditure)); HEXP (Current health expenditure (% of GDP)); WATER (People using at least basic drinking water services (% of population)); DEF (People practicing open defecation (% of population)); SANI (People using at least basic sanitation services (% of population)); PM (PM2.5 air pollution, mean annual exposure (micrograms per cubic meter)). Figure S4. Spatial varying coefficients for the effects of development indicators on under-five mortality rates in the year 2017 showing countries with significant observed effects (95% Credible Interval). EDU (Secondary education, pupils (% female)); EMP (Employment to population ratio, 15+, female (%) (ILO estimate)); GDP (Gross domestic product—per capita (current US$)); GNE (Gross national expenditure (% of GDP)); OHP (Out-of-pocket expenditure (% of current health expenditure)); HEXP (Current health expenditure (% of GDP)); WATER (People using at least basic drinking water services (% of population)); DEF (People practicing open defecation (% of population)); SANI (People using at least basic sanitation services (% of population)); PM (PM2.5 air pollution, mean annual exposure (micrograms per cubic meter)). Table S4. Number of countries with missing information from 1960 to 2019 by study variables Number of countries with missing data for all available years by the study variables.
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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 | 0.002 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.729 | 0.079 |
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".