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Record W1999097139 · doi:10.1017/cbo9780511801006.021

Immunities

2007· book-chapter· en· W1999097139 on OpenAlexaff
Robert Cryer, Håkan Friman, Darryl Robinson, Elizabeth Wilmshurst

Bibliographic record

VenueCambridge University Press eBooks · 2007
Typebook-chapter
Languageen
FieldSocial Sciences
TopicGlobal Maritime and Colonial Histories
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsComputer science

Abstract

fetched live from OpenAlex

Introduction Overview The law of immunities has ancient roots in international law, extending back not hundreds, but thousands, of years. In order to maintain channels of communication and thereby prevent and resolve conflicts, societies needed to have confidence that their envoys could have safe passage, particularly in times when emotions and distrust were at their highest. Domestic and international law developed to provide both inviolability for the person and premises of a foreign State's representatives and immunities from the exercise of jurisdiction over those representatives. While immunities are valuable in preventing interference with representatives, and thereby maintaining the conduct of international relations, they can also lead to serious injustice. In recent decades, with the advent of the human rights movement, States have taken stronger and stronger steps to prosecute international criminals. This emboldened State practice has brought to the fore many hidden or unresolved questions as to the boundaries between principles of accountability and immunity, and has engendered a reassessment and restriction of the scope of immunities. The resulting jurisprudence and authorities may at first glance appear perplexing, contradictory, confused or incoherent. This chapter will suggest that the authorities in fact present a consistent and coherent set of rules, and that those rules reflect an underlying balancing between competing international ‘goods’. To understand the jurisprudence and its significance, it will be necessary (1) to appreciate the underlying principles and protected values, (2) to distinguish between ‘functional’ immunities and ‘personal’ immunities and (3) to distinguish between national and international courts.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.042
Threshold uncertainty score0.000

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.004
Scholarly communication0.0030.003
Open science0.0010.002
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0420.014

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 head0.035
GPT teacher head0.235
Teacher spread0.200 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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

Citations0
Published2007
Admission routes1
Has abstractyes

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