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Record W3121302530 · doi:10.1017/s0922156517000036

Crafting and Promoting International Crimes: A Controversy among Professionals of Core-Crimes and Anti-Corruption

2017· article· en· W3121302530 on OpenAlexaff
Mikkel Jarle Christensen

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
KeywordsGenocideCrimes against humanityWar crimePolitical scienceCONTESTPrinciple of legalityCriminologyPoliticsLanguage changeCorporate governanceLawSociologyInternational lawManagement

Abstract

fetched live from OpenAlex

Abstract The emergence of new international criminal courts in the 1990s intensified an existing professional contest to define international crimes. This ongoing competition concerned which crimes should be termed international and consequently become the subject of international institution-building and prosecution. The article draws upon Pierre Bourdieu's analytical tool of the ‘field’ in order to investigate how legal professionals located in different fields of practice have crafted and promoted specific crimes as international, in successive phases. The focus of the analysis is on two stages of this development. The first is the protracted emergence of a field of ‘core crimes’ centred on a specific set of crimes: genocide, crimes against humanity, and war crimes. The second is an emergent contestation of this focus on ‘core crimes’ embedded in the careers of legal professionals engaged in the field of anti-corruption. By adapting the impactful narratives developed around core crimes, this second phase of contestation becomes a new frontline in the wider endeavour to define the role of criminal law in a larger international space of governance and politics.

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)0.015
metaresearch head score (Gemma)0.019
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.019
Threshold uncertainty score0.081

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0150.019
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0190.056
Scholarly communication0.0160.007
Open science0.0010.008
Research integrity0.0050.006
Insufficient payload (model declined to judge)0.0030.000

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 head0.030
GPT teacher head0.362
Teacher spread0.331 · 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
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

Citations10
Published2017
Admission routes1
Has abstractyes

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