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Record W2246679405

Some Lessons on Complementarity for the International Criminal Court Review Conference

2010· article· en· W2246679405 on OpenAlexaff
Nidal Nabil Jurdi

Bibliographic record

VenueSSRN Electronic Journal · 2010
Typearticle
Languageen
FieldSocial Sciences
TopicInternational Law and Human Rights
Canadian institutionsMcGill University
Fundersnot available
KeywordsImpunityComplementarity (molecular biology)StatutePolitical scienceLawCriminal courtParagraphLaw and economicsInternational lawSociologyHuman rights
DOInot available

Abstract

fetched live from OpenAlex

The ICC practice in the last seven years disclosed a number of contentious issues within the legal and practical parameters of complementarity. The complementarity regime serves as a system to encourage and facilitate the compliance of states with their responsibility to investigate and prosecute international crimes. However, this has not been materialized in practice. The ipso facto admissibility of cases of inactions proved to be damaging to positive complementarity. It opens the door widely for overloading the ICC with many cases of no added value for the ICC and for ending impunity. Article 17 has been interpreted narrowly by the Prosecutor and the Chambers, and this interpretation and policy needs to be amended. In this regard, some amendments to Article 17 could be suggested; mainly to correlate explicitly Article 17 to preambler paragraph six of the Statute, and to use judicial and non-judicial tools to encourage national jurisdictions to prosecute. There are number of challenges that are awaiting the ICC Review Conference in Uganda in 2010. It will be a huge missed opportunity if complementarity is not discussed and addressed in this Conference. The hope is that the Review Conference will courageously address this vital issue to revive the path of the ICC in the direction of encouraging national jurisdictions to contribute to ending impunity, while the ICC remains vigilant to ensure justice is done in conformity with the ICC Statute.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
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 score0.835
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
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 science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.062
GPT teacher head0.385
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.

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

Citations17
Published2010
Admission routes1
Has abstractyes

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