A new innovative method to measure the cost of war: future with fewer conflicts via harm reduction approaches
Bibliographic record
Abstract
BACKGROUND: The destruction of World War I (WWI) and World War II (WWII) changed the world forever. In this analysis, the economic costs of WWI and WWII are considered via a harm reduction approach to highlight the cost of war via the mortality of military personnel. The harm reduction philosophy and homeostasis of a biological cell are utilized as a pragmatic approach and analogy to give a greater context to the findings, despite the omission of civilian casualties and military disabilities. METHODS: Tangible (e.g., loss of wages, productivity, and contributions) and intangible (e.g., quality of life) costs are estimated based on the value of each military personnel derived from secondary data and a mathematical model. This is the first study to estimate the cost of war based on soldier's mortality during the first and second World War. RESULTS: Based on the tangible value, the WWI and WWII cost for the military personnel was US$43.204 billion ($13 billion ≤ α ≤ $97 billion) and US$540.112 billion ($44 billion ≤ α ≤ $1 trillion). When the intangible cost is considered, it is estimated that the WWI cost was beyond US$124 trillion ($43 trillion ≤ β ≤ $160 trillion), and the WWII cost was above US$328 trillion ($115 trillion ≤ β ≤ $424 trillion). The sensitivity analyses conducted for WWI and WWII demonstrate different ranges based on tangible and intangible values. CONCLUSIONS: In the current climate of increasing hostilities, inequalities, global warming, and an ever-changing world, economic prosperities are directly linked to peace, stability, and security. Therefore, any future decisions for military conflicts need to increasingly consider harm reduction approaches by considering the cost of life and potential disabilities for each nations' soldiers, sailors, and pilots.
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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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".