Bibliographic record
Abstract
As the COVID-19 pandemic clearly illustrated, the well-being of populations is shaped not just by the health systems that generate diseases but also by the macro structural factors that influence the ecosocial determinants of health. Wars are among the most severe threats to public health, triggering widespread mortality, the collapse of health care systems, food insecurity, disease outbreaks, psychological trauma, and long-term socio-economic destabilization, as well as other forms of social and environmental destruction. This article explores the current geopolitical landscape, analyzing political tensions and the major causes of conflicts in order to evaluate the potential of a Third World War developing in the near future. It argues that, from a public health perspective, comprehending the geopolitical motivations behind armed conflicts is crucial for their prevention. Given the current era of escalating geopolitical tensions and the looming threat of nuclear conflict, the authors urge public health institutions and their educators and researchers to engage deeply with war and conflict as a determinant of health and health inequity, and advocate for peace through diplomacy and disarmament.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".