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Record W2777059430 · doi:10.7202/1046514ar

Reconciling Indigenous peoples with the judicial process: An examination of the recent genocide and sexual slavery trials in Guatemala and their integration of Mayan culture and customs

2018· article· en· W2777059430 on OpenAlexvenueno aff
Elisabeth Madeleine Patterson

Bibliographic record

VenueRevue québécoise de droit international · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicPolitics and Society in Latin America
Canadian institutionsnot available
Fundersnot available
KeywordsGenocideTribunalIndigenousCrimes against humanityLawCriminologyPolitical scienceInternational lawSociologyWar crime

Abstract

fetched live from OpenAlex

This article examines two recent landmark cases in Guatemala. The first one is the 2013 Rios Montt genocide case, which led to one of the first convictions of a former Head of State for genocide in a national court. The second one is the 2016 Sepur Zarco case, which marked the first time former military commanders were convicted in a national court of crimes against the duties of humanity for sexual and domestic slavery. In both cases, almost all the victims were Indigenous. The author was present for parts of both trials as an international observer and interviewed individuals directly involved in the prosecution. Considering that Guatemalan and international law require that legal decisions give due consideration to the customs of the Indigenous peoples concerned, the article assesses to what extent Indigenous culture was taken into account during the trial and how Indigenous concepts and customs were considered in the judgements. In both cases, the tribunal did not modify usual court procedures, except to provide interpreters for the testimony of the unilingual Q’eqchi and Ixil witnesses. Both judgements did, however, take into account several concepts and customs from the Mayan worldview and these were key to the Court’s reasoning leading to the guilty verdicts.

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)0.002
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 score0.348
Threshold uncertainty score0.985

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.026
GPT teacher head0.306
Teacher spread0.280 · 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

Citations13
Published2018
Admission routes1
Has abstractyes

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