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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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.037
metaresearch head score (Gemma)0.046
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.037
Threshold uncertainty score0.197

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0370.046
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0160.047
Scholarly communication0.0240.028
Open science0.0020.014
Research integrity0.0090.010
Insufficient payload (model declined to judge)0.0070.001

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.

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 source (direct Gemma or distilled Codex), 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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