Bibliographic record
Abstract
Although international law forbids nuclear attacks, only nine states have mutually assured destruction available to prevent direct attacks against themselves, while non-nuclear states have few substantive options to deter a nuclear attack. This study analyzes the economic impacts of a theoretical international agreement that eliminates patent rights for any nuclear aggressor (i.e., free global compulsory licensing of all intellectual property (IP) for a nuclear aggressor). The results found that all but one of the nuclear states would have a significant economic disincentive to start a nuclear attack if the proposal was put into force. Payback times ranged from 1.2 to 40 years, where the entire GDP of a nuclear aggressor would be needed to offset the loss for aggression, indicate such a mechanism as a whole would be an effective nuclear deterrent. This method would not be universally effective without ensuring all nuclear states are members of the international economy and IP processes. With the growth of open-source products and reduced value of patents, this mechanism does have a limited effectiveness time. Currently it appears to be a policy trajectory worthy of future work that can enhance safety from nuclear threat without causing harm to countries of goodwill.
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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".