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Record W2902513016 · doi:10.5539/jpl.v11n4p40

International Law, Sovereignty and the Responsibility to Protect: An Overview

2018· article· en· W2902513016 on OpenAlexvenueno aff
Thomas Prehi Botchway

Bibliographic record

VenueJournal of Politics and Law · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicGlobal Peace and Security Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsSovereigntyResponsibility to protectInternational lawLawCommissionState responsibilityState (computer science)Political scienceIntervention (counseling)Law and economicsSubject (documents)Public international lawSovereign stateSociologyPsychologyPoliticsComputer science

Abstract

fetched live from OpenAlex

This paper is an attempt at analysing the intricacies between international law, the concept of Responsibility to Protect and its implications for the sovereignty of modern states. The paper examines how the concept of responsibility to protect (as stipulated by the International Commission on Intervention and State Sovereignty (ICISS)) impacts on the sovereignty of states. It adopts the essay style of writing and reviews a number of documents on the subject of international law, sovereignty and the responsibility to protect. The paper consequently argues that though the ICISS claims that its “purpose is not to license aggression with fine words, or to provide strong states with new rationales for doubtful strategic designs” (ICISS, 2001, p. 35), the Commission’s very attempt to exempt the permanent five and other so-called major powers from intervention does just that whether intentionally or unintentionally. It consequently recommends that much effort should be made to address the inequalities within the international system through the formulation of appropriate policies and international regulations that address the sovereign equality of states in the international system, especially on the question of intervention.

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)0.001
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: Empirical
Teacher disagreement score0.332
Threshold uncertainty score0.411

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.033
GPT teacher head0.356
Teacher spread0.323 · 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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