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Record W3014602299 · doi:10.1093/jicj/mqaa014

Prosecuting the Nexus between Terrorism, Conflict-related Sexual Violence and Trafficking in Human Beings before National Legal Mechanisms

2020· article· en· W3014602299 on OpenAlexaff
Anne-Marie de Brouwer, Eefje de Volder, Christophe Paulussen

Bibliographic record

VenueJournal of International Criminal Justice · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicGender, Security, and Conflict
Canadian institutionsImpact
Fundersnot available
KeywordsNexus (standard)TerrorismImpunityCriminologySexual violencePolitical scienceHuman rightsContext (archaeology)LawSociologyEngineering

Abstract

fetched live from OpenAlex

Abstract United Nations (UN) Security Council Resolution 2331 (2016) recognizes that ‘acts of sexual and gender-based violence, including when associated to trafficking in persons, are known to be part of the strategic objectives and ideology of certain terrorist groups, used as a tactic of terrorism and an instrument to increase their finances and their power through recruitment and the destruction of communities’. In the same resolution, the Council noted that such trafficking, particularly of women and girls, ‘remains a critical component of the financial flows to certain terrorist groups’ and is ‘used by these groups as a driver for recruitment’. Boko Haram and Al-Shabaab are among the main terrorist groups that have used human trafficking (including for sexual exploitation) and conflict-related sexual violence as tactics of terrorism, or ‘sexual terrorism’. This article will: (i) explain the nexus between these three crimes; (ii) focus on its different manifestations in the context of these terrorist organizations; and (iii) reflect on the possibilities for national criminal prosecution. To assist in the fight against impunity and increase accountability, this article provides suggestions to facilitate the successful prosecution of sexual terrorism in a more survivor-centric way.

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)0.001
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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.237
Threshold uncertainty score0.490

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.056
GPT teacher head0.344
Teacher spread0.288 · 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 designQualitative
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

Citations3
Published2020
Admission routes1
Has abstractyes

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