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Record W4365812795 · doi:10.1163/17087384-bja10078

Credible Commitment or Sham Devotion to the International Criminal Court: Whither Nigeria, Kenya and Uganda?

2023· article· en· W4365812795 on OpenAlexvenueno aff
Sylvester Ndubuisi Anya, Adrian Osuagwu, Emmanuel Onyeabor, Joycelin Chinwe Okubuiro, Matthew Nwankwo, Daniel Onyeonagu, Ifeoma Pamela Enemo

Bibliographic record

VenueAfrican Journal of Legal Studies · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicInternational Law and Human Rights
Canadian institutionsnot available
Fundersnot available
KeywordsCredibilityStatuteCriminal courtSustainabilityPolitical scienceLawHomogeneousDevelopment economicsInternational lawSocioeconomicsSociologyEconomicsBiology

Abstract

fetched live from OpenAlex

Abstract There is a pattern of inconsistent behaviour among some African states towards the International Criminal Court ( ICC ) showing credible commitment and sham devotion. This study poses a question: what will be of the status of Nigeria, Kenya and Uganda (the Triad) vis-à-vis the ICC by the end of 2023, considering the pattern of behaviour of these states so far, evidence from data and the spate of domestication and exit procedures in homogeneous African states in contemporary times? The objectives of the study are to predict the status of the Triad by 2023 and reflect on the implication of the predictions for the on-going viability, sustainability and credibility challenges facing the ICC in Africa. The study finds that: the pattern of Nigeria’s behaviour shows sham devotion and predicts that she will not domesticate the Rome Statute come 2023; the pattern of Kenya’s behaviour straddles credible commitment and sham devotion and she may maintain her membership of the ICC by 2023; the pattern of Uganda’s behaviour shows fairly credible commitment mixed with sham devotion and she will maintain her membership of the Statute come 2023. These predictions have implications on the ongoing viability, sustainability and credibility challenges facing the ICC 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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.675
Threshold uncertainty score0.547

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 studies0.0010.000
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 head0.072
GPT teacher head0.361
Teacher spread0.289 · 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 designNot applicable
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

Citations8
Published2023
Admission routes1
Has abstractyes

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