Bibliographic record
Abstract
Studies of health have recognized the influence of socioeconomic position on health outcomes. People with higher socioeconomic ranking, in general, tend to be healthier than those with lower socioeconomic rankings. The effect of political environment on population health has not been adequately researched, however. This study investigates the effect of democracy (or lack thereof) along with socioeconomic factors on population health. It is maintained that democracy may have an impact on health independent of the effects of socioeconomic factors. Such impact is considered as the direct effect of democracy on health. Democracy may also affect population health indirectly by affecting socioeconomic position. To investigate these theoretical links, some broad measures of population health (e.g., mortality rates and life expectancies) are empirically examined across a spectrum of countries categorized as autocratic, incoherent, and democratic polities. The regression findings support the positive influence of democracy on population health. Incoherent polities, however, do not seem to have any significant health advantage over autocratic polities as the reference category. More rigorous tests of the links between democracy and health should await data from multi-country population health surveys that include specific measures of mental and physical morbidity.
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 | 0.007 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.011 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".