The Future of Arms Control in a Multilateral and Multi-Domain Environment
Bibliographic record
Abstract
The crisis of arms control is obvious and broadly discussed among states, within the world’s expert community and to a lesser extent the media. This crisis has at least three building blocks: Russia continues to violate or undermine key arms control treaties and commitments; China rejects to join the existing arms control architecture; and both countries heavily invest in the modernization of their armed forces, including development of the nuclear arsenals. In the current highly competitive environment, arms control is more difficult to achieve and is likely to accomplish less than what was optimistically anticipated a generation ago. The growing pressure to “save arms control at all cost”, often expressed by the Western expert community, further complicates the situation. The excessively aspirational and ideological approach to arms control – in which arms control, disarmament, and non-proliferation (ADN) become a silver bullet solution – is as dangerous as security and defence policies which entirely exclude ADN. As James Cameron rightly points out, “history should teach policy-makers to look beyond formulae for strategic stability to other ways in which arms control can help to contain disruptive challenges to the balance of power and minimize the chances of war”.
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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.011 | 0.015 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.005 | 0.005 |
| 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".