Towards designing systems to preserve peace by putting a cost on war
Bibliographic record
Abstract
We are observing constant progress in many technical domains that influence the world on many socio-economic and socio-technical levels. Recent rapid development of decentralized solutions redefining the digital economy, or the current boom in AI are already restructuring our ways of thinking. Despite technological prosperity we have not progressed socially to the point where war would become obsolete. We are not day dreamers, and we do not think that we can eradicate war with technology. However, we do think that decentralized technology has reached a level where it can be used to enforce proper disincentives. In this paper we are presenting a Cost of War architecture that combines an array of recent technological advancements and literally puts a cost on war. We are proposing a system that proactively and retroactively calculates economic losses of an invaded country, provides a privacy-preserving and decentralized way of identifying citizens, and enables processing of their restitution and compensation claims. We believe that it is the first system that tries to address this unsettling global problem.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.007 | 0.013 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".