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Record W2591591971 · doi:10.1007/978-94-6265-171-5_5

Applying the Tools

2017· book-chapter· en· W2591591971 on OpenAlexaff
Cassandra Steer

Bibliographic record

VenueInternational criminal justice series · 2017
Typebook-chapter
Languageen
FieldSocial Sciences
TopicComparative and International Law Studies
Canadian institutionsMcGill University
Fundersnot available
KeywordsArbitrarinessCLARITYTerminologyConsistency (knowledge bases)Unitary stateLiabilityLawProcess (computing)Political scienceFormalism (music)Criminal lawLaw and economicsSociologyEpistemologyComputer scienceLinguisticsPhilosophy

Abstract

fetched live from OpenAlex

The process of legal patchworking (drawing on multiple domestic criminal law notions as a direct source of ICL) is a dynamic process involving judges, practitioner lawyers and scholars. However this patchworkingPatchworking process has rarely taken place with attention to methodology, rather it has been arbitrary, depending upon which participants are most influential in a given forum, and what their preference is for specific domestic systems of liability. What has emerged is a lack of clarity and a clash of legal cultures, undermining both the predictability required for defendants as well as the consistency required for a stable and functioning body of criminal law. In this chapter comparative law tools are offered as a methodMethod, comparative for understanding the process, and for undertaking an analysis of the different domestic modes of liability from which international tribunals draw. A consistent comparative methodComparative method can help temper the arbitrariness and clash of legal cultures. In this chapter the terminology applied throughout the comparative study in Part II is also laid out, including formalism, complicityComplicity , unitary and differentiated systems, objectivityObjectivity and subjectivitySubjectivity , guilt and responsibility.

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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.934
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.146
GPT teacher head0.386
Teacher spread0.239 · 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
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
Published2017
Admission routes1
Has abstractyes

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