An Analysis of the Security Council Procedures in Managing Severe Violations of the International Human Rights
Bibliographic record
Abstract
The UN Security Council is primarily in charge of maintaining international peace and security. There has been raised various debates on how the Security Council manage international crises in the world, particularly severe violations of international human rights law. On the one hand, the traditional view to international peace has altered and the Security Council is also in charge of observing the standards of the international human rights. On the other hand, the international community has faced inconsistencies in the use of the veto by its permanent members on the international human rights violations. However, many analysts believe that the Security Council could take timely action to prevent violations of international human rights law. At that time, they fell into the trap of politics and proved insufficient. This article is in response to this important issue, indicating that the management of the Security Council has been fair in the case of international human rights violations. This study also aims to investigate whether the Security Council has been successful in adopting a procedure independent from the interests of its permanent members.
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 | 0.029 | 0.068 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.010 | 0.011 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".