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Record W2567262328 · doi:10.1017/s0922156516000741

Symbolism as a Constraint on International Criminal Law

2017· article· en· W2567262328 on OpenAlexaff
Marina Aksenova

Bibliographic record

VenueLeiden Journal of International Law · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicInternational Law and Human Rights
Canadian institutionsCentre for International Governance Innovation
Fundersnot available
KeywordsConstraint (computer-aided design)Political scienceLawCriminal lawCriminal procedureLaw and economicsSociologyMathematics

Abstract

fetched live from OpenAlex

Abstract International criminal law is being pulled in different directions by various conflicting considerations – deterrence, retribution, justice for victims, reconciliation, and setting the historical record. This trend is detrimental to the survival of the system as it erodes coherence and undermines its legitimacy. One may suggest that international criminal law needs a principle objective to bring order to the system. This article argues that while this statement may be true, it is equally important to have a discussion about pragmatic policy choices underlying the system. Acknowledging that the role of international criminal law is symbolic assists with constraining over-ambition implicit in the discipline. Treating symbolism as a policy consideration places necessary checks on other goals proclaimed by international courts and UN executive bodies and also serves as a tool informing the exercise of discretion in international criminal law.

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)0.010
metaresearch head score (Gemma)0.020
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.016
Threshold uncertainty score0.051

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.020
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0060.051
Scholarly communication0.0160.009
Open science0.0010.013
Research integrity0.0030.007
Insufficient payload (model declined to judge)0.0100.001

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 head0.032
GPT teacher head0.356
Teacher spread0.324 · 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 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

Citations16
Published2017
Admission routes1
Has abstractyes

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