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Record W2989042041 · doi:10.1093/jicj/mqz040

The Malabo Protocol, the ICC, and the Idea of ‘Regional Complementarity’

2019· article· en· W2989042041 on OpenAlexaff
Sarah Nimigan

Bibliographic record

VenueJournal of International Criminal Justice · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicInternational Law and Human Rights
Canadian institutionsWestern University
Fundersnot available
KeywordsComplementarity (molecular biology)StatuteConstructiveLawCriminal courtPolitical scienceCriminal justiceInternational lawLaw and economicsCornerstoneSociologyProcess (computing)Computer science

Abstract

fetched live from OpenAlex

Abstract The African Union (AU) has taken steps to regionalize international criminal law through the expansion of the African Court of Justice and Human Rights (ACJHR) vis-à-vis the Malabo Protocol. The principle of complementarity is a cornerstone of the Rome Statute of the International Criminal Court (ICC). The Rome Statute crystallizes a complementary relationship between the ICC and domestic legal systems under Article 17 but makes no mention of regional or ad hoc jurisdictions. Prospects for including regional jurisdictions within the principle of complementarity are contingent upon a positive judicial interpretation of the principle and clearly established obligations at each level. It will necessarily require funding and support by states. Such an approach will contribute to the ongoing development of a robust system of international criminal justice. In order to effectively resolve the issue of competing mandates and effective domestic implementation, a cooperative model needs to be espoused. Although hypothetical at present, the idea of ‘regional complementarity’ is one worth thinking about in the context of constructive reform at the ICC. The prospective ACJHR offers a useful framework to analyse the potential role of regional mechanisms within the international criminal law project, broadly considered.

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)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.865
Threshold uncertainty score0.519

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.034
GPT teacher head0.365
Teacher spread0.331 · 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

Citations9
Published2019
Admission routes1
Has abstractyes

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