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Record W2888855587 · doi:10.5539/jpl.v11n3p53

Developing International Law in Challenging Times

2018· article· en· W2888855587 on OpenAlexvenueno aff
Thomas Prehi Botchway, Abdul Hamid Kwarteng

Bibliographic record

VenueJournal of Politics and Law · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicInternational Law and Human Rights
Canadian institutionsnot available
Fundersnot available
KeywordsInternational lawPolitical scienceLawCompliance (psychology)TerrorismSoft lawVetoLaw and economicsSociology

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.710
Threshold uncertainty score0.161

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.031
GPT teacher head0.334
Teacher spread0.303 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations7
Published2018
Admission routes1
Has abstractyes

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