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Record W4409477496 · doi:10.7202/1117432ar

L’activité médico-légale comme outil et objet du processus mémoriel au Guatemala

2024· article· fr· W4409477496 on OpenAlexvenueno aff
Clara Duterme

Bibliographic record

VenueAnthropologie et Sociétés · 2024
Typearticle
Languagefr
FieldSocial Sciences
TopicGlobal Security and Public Health
Canadian institutionsnot available
FundersAgence Nationale de la Recherche
KeywordsPhilosophyPolitical science

Abstract

fetched live from OpenAlex

Cet article étudie les modalités de traitement des restes humains des victimes du conflit armé produits par l’activité médico-légale au Guatemala depuis les années 1990 et la manière dont ils ont été mobilisés dans le processus de mémorialisation de la violence politique passée. Ces restes humains sont ceux de populations civiles, majoritairement autochtones, victimes d’une politique de terreur instaurée par les gouvernements militaires dans les années 1975-1983. Après le retour à la paix, la réticence persistante des institutions nationales va conduire les acteurs de la société civile à assumer seuls la prise en charge funéraire et mémorielle des restes exhumés. Leur collaboration avec les anthropologues médico-légaux, qui se poursuit depuis une trentaine d’années, a vu l’intégration toujours plus forte de la pratique médico-légale au processus mémoriel. D’abord mobilisée pour mettre au jour des traces matérielles de la violence, endossant par la suite un rôle actif dans les processus de requalification et la prise en charge funéraire des morts exhumés, la pratique de l’exhumation est finalement elle-même intégrée dans les éléments mémorialisés.

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)0.001
metaresearch head score (Gemma)0.005
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 score0.138
Threshold uncertainty score0.275

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0070.005
Scholarly communication0.0020.001
Open science0.0010.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.000

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 head0.281
GPT teacher head0.560
Teacher spread0.279 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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