Bibliographic record
Abstract
Democracy is a system where citizens choose leaders through free and fair elections, founded on the principle of equality and aimed at prioritizing public health and well-being.1 When done properly, democracy can benefit population health through multiple mechanisms such as prioritizing public health, holding leaders accountable and fostering competent governance.2 Nevertheless, research on the relationship between democracy and health presents a puzzling picture. First, evidence is mixed regarding whether democracy is associated with improved health.3 On one hand, many studies suggest democracy has a positive impact on a wide range of population health outcomes, including life expectancy, infant and child mortality, self-rated health.2 On the other hand, a growing literature has also raised doubts and found democracy has little to no impact on health outcomes.4 Furthermore, the significant increase in democratic governance around the world has also been accompanied by the persistence and widening of health disparities, which is regarded as ‘one of the great disappointments in public health’.5, (page 761) In Canada, for example, the paradox is that, despite significant gains in life and health expectancy—ranking the country among the world’s healthiest—inequalities in these measures persist and have even widened over time.6 In fact, there is a well-documented public health puzzle. That is, in highly developed social democratic and welfare states such as Denmark, Finland, Norway and Sweden health inequalities, both relative and absolute, are not necessarily smaller and, in some instances, may even be more pronounced.5,7
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 | 0.026 | 0.105 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.019 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.010 | 0.013 |
| Insufficient payload (model declined to judge) | 0.019 | 0.002 |
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".