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Record W2953204468 · doi:10.1163/17087384-12340029

Strengthening States’ and the International Community’s Responsibility to Protect Civilians: Revisiting the Prosecution of War Crimes Committed in Africa by the International Criminal Court (ICC)

2018· article· en· W2953204468 on OpenAlexvenueno aff
Seun Bamidele

Bibliographic record

VenueAfrican Journal of Legal Studies · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicGlobal Peace and Security Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsWar crimeLawGenocidePolitical scienceCriminalizationInternational lawSpanish Civil WarCriminologyCrimes against humanityInternational communityTerrorismSociologyPolitics

Abstract

fetched live from OpenAlex

Abstract The silhouette of International Criminal Justice ( ICJ ) is fast changing across the globe. The change and transformation are connected to the criminalization of war, which has complicated the attraction of and engagement in the war for war-mongers. At least, the last few years had seen remarkable prosecution of war criminals in Africa. This is related to a relatively new thinking that informed the establishment of International Criminal Court ( ICC ) and global re-enforcement of war crime-related charges. Since the genocide in Rwanda, the establishment of the ICC has led to the prosecution of warlords. Also, the ICC has issued thirteen public warrants of arrest on war charges to actors and perpetrators in more than four African states. The case of President of Sudan, whose warrant of arrest had been issued regarding the crisis in Darfur, demonstrated that African leaders and war-mongers would be held responsible for their actions and atrocities they have committed. The lesson from the ICC is clear, war-mongers would be made to pay for their criminality. This article intends to examine the actions of the ICC on intra-state civil war crimes in Africa and assess whether ICC can act as deterrence on for intrastate war mongers in Africa.

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)0.009
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.084
Threshold uncertainty score0.762

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.002
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.040
GPT teacher head0.332
Teacher spread0.292 · 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 designQualitative
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

Citations2
Published2018
Admission routes1
Has abstractyes

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