Developing International Law in Challenging Times
Bibliographic record
Abstract
The challenges confronted by the world in the 21st century are enormous; from the massive outflow of refugees, the threat of terrorism, the need for a general consensus to protect the environment, etc. There is thus the need for scholars, practitioners, and stakeholders of international law to think of effective and efficient ways of developing robust and strong international laws to deal effectively with these challenges.Using the qualitative approach to research, this paper examines some of the key challenges that confronts the development of and compliance with international law. The paper offers some new insights which have the propensity to aid in the development of and compliance with international law in these challenging times.The paper concludes that though international law has over the years expedited addressing most of the world’s challenges, the recent challenges requires modifications of some aspects of existing international laws to effectively deal with such challenges. For instance, there is the need to review the veto power of the five permanent members of the UN Security Council; there must be better interpretation of the law that prohibits the use of force, as well as the need for appropriate measures to convince states that abiding by international law is a win-win game. In addition, deploying economic diplomacy and applying the Corporate Social Responsibility Approach to Building International Law (CRASBIL) are deemed meaningful for developing international law and also achieving effective compliance.
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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 | 0.037 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.016 | 0.047 |
| Scholarly communication | 0.024 | 0.028 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.009 | 0.010 |
| Insufficient payload (model declined to judge) | 0.007 | 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".