Humanization of Arms Control: Paving the Way for a World free of Nuclear Weapons
Bibliographic record
Abstract
Despite clear legal rules and political commitments, no significant progress has been made in nuclear disarmament for two decades. Moreover, not even the use of these weapons has been banned to date. New ideas and strategies are therefore necessary. The author explores an alternative approach to arms control focusing on the human dimension rather than on States’ security: humanization of arms control! The book explores the preparatory work on arms control treaties and in particular the role of civil society. It analyzes the positive experiences of the movements against chemical weapons, anti-personnel mines, and cluster munitions, as well as the recent conclusion of the Arms Trade Treaty. The author examines the question of whether civil society will be able to replicate the success strategies that have been used, in particular, in the field of anti-personnel mines (Ottawa Convention) and cluster munitions (Oslo Convention) in the nuclear weapons field. Is there any reason why the most destructive weapons should not be outlawed by a legally binding instrument? The book also explains the effects of weapons, especially nuclear weapons, on human beings, the environment, and global development, thereby focusing on vulnerable groups, such as indigenous peoples, women, and children. It takes a broad approach to human rights, including economic, social, and cultural rights. The author concludes that the use of nuclear weapons is illegal under international humanitarian and human rights law and, moreover, constitutes international crimes under the Rome Statute of the International Criminal Court. In his general conclusions, the author makes concrete proposals for the progress toward a world without nuclear weapons.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.030 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".