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Record W4318834073 · doi:10.1093/jhuman/huac067

Immunities of Foreign Officials for International Crimes: The Dilemmas of Strategic Litigation

2023· article· en· W4318834073 on OpenAlexaff
Frédéric Megret

Bibliographic record

VenueJournal of Human Rights Practice · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicInternational Law and Human Rights
Canadian institutionsMcGill University
Fundersnot available
KeywordsHuman rightsPolitical scienceLawArgument (complex analysis)Norm (philosophy)Law and economicsSociology

Abstract

fetched live from OpenAlex

Abstract This article analyses the elusive search to restrict immunities for foreign officials accused of international crimes as a form of strategic litigation. It emphasizes how litigation ‘constitutes’ legal reality beyond particular victories. Problematizing ‘success’ in litigation makes it possible to pay attention to unintended effects and even perverse outcomes of certain strategic routes. A proper understanding of success as more than victory is then used to assess three routes that have been used to try and limit, circumvent or oppose functional immunities of former officials. These routes are found not to be of even value for human rights, independently of their legal validity or their odds of success: the idea that international crimes are not committed as part of state functions trivializes the ‘public’ character of most international crimes; the argument that the jus cogens norm that prohibits international crimes trumps immunities either constantly needs to be finessed or sets up too dramatic a showdown; the idea that immunities offend human rights finds some favour as the one that is closest to the heart of human rights, although its tendency to symbolically sanctify the right to prosecutions could also have problematic ripple effects. Offered as a contribution to thinking through the rights implications of litigation, the article insists on the responsibility of human rights lawyers in creating legal worlds of their own making.

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)0.001
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 score0.177
Threshold uncertainty score0.513

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
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 head0.126
GPT teacher head0.408
Teacher spread0.282 · 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

Citations6
Published2023
Admission routes1
Has abstractyes

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