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Record W3122374407

Forced Marriage and the Special Court for Sierra Leone: Legal Advances and Conceptual Difficulties

2011· article· en· W3122374407 on OpenAlexaff
Valerie Oosterveld

Bibliographic record

VenueSSRN Electronic Journal · 2011
Typearticle
Languageen
FieldArts and Humanities
TopicHistorical and Contemporary Political Dynamics
Canadian institutionsWestern University
Fundersnot available
KeywordsForced marriageSierra leoneForced migrationLawCrimes against humanityPolitical scienceJurisprudenceSpecial courtAmnestyExtortionHuman rightsSociologyCriminologyRefugeeTribunalWar crimeInternational law
DOInot available

Abstract

fetched live from OpenAlex

Forced marriage was endemic during the Sierra Leonean conflict. Girls and women forced to serve as 'wives' to rebel soldiers were usually expected to submit to ongoing rape and to provide domestic labour to their 'husbands'. Many of these 'wives' suffer from continuing stigmatization. The Prosecutor of the Special Court for Sierra Leone brought forced marriage charges as a crime against humanity through the category of inhumane acts against Brima, Kamara and Kanu, affiliated with the Armed Forces Revolutionary Council (AFRC), and Sesay, Kallon and Gbao, affiliated with the Revolutionary United Front (RUF). This article considers two benefits stemming from the resulting jurisprudence: the naming of forced marriage as an inhumane act and the acknowledgement of forced marriage as a violation not captured by other legal terms. However, conceptual difficulties remain: how should forced marriage be defined so as to fulfil the principle of nullum crimen sine lege? Is forced marriage more accurately labelled as enslavement? And, is conjugality accurately captured as a defining feature of forced marriage? If forced marriage is to be successfully prosecuted in other contexts - for example, in the Extraordinary Chambers in the Courts of Cambodia - then more attention must be paid to resolving these questions.

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)0.000
Version: codex-gemma-dda1882f352aValidation 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: Empirical
Teacher disagreement score0.461
Threshold uncertainty score0.522

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.017
GPT teacher head0.205
Teacher spread0.188 · 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 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

Citations0
Published2011
Admission routes1
Has abstractyes

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