Bibliographic record
Abstract
The rise of democracy across the world brought with it expectations that governments would be more attentive and responsive to the welfare of the people, creating better services and better health. Indeed, a considerable body of scholarship finds that democracy has significant, direct effects on multiple measures of well-being, particularly life expectancy and infant mortality. Despite several recent critiques, the paramount theme is that democracy is good for health. This study contributes to this literature by assessing the relationship between democracy and child diarrhea and malnutrition across 52 developing countries. Using a multilevel modeling strategy, the analysis examines the country-level effects of democracy and development on child health, while simultaneously taking into account wealth, education, and other household characteristics at the individual level. Contrary to much previous scholarship, democracy does not exhibit a significant association with diarrhea or malnutrition. Instead, gross domestic product (GDP) per capita and improved sanitation and water have substantial effects on child health at the country level. At the individual level, household wealth and maternal education have the largest health-enhancing impact on child diarrhea and malnutrition. Furthermore, the size and strength of the relationship between wealth and health does not vary by political regime. These results demonstrate the enduring importance of socioeconomic status regardless of political context, and they support a small but growing literature that calls the democracy–health link into question.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".