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Record W2593539582 · doi:10.7202/1038900ar

Comprendre la monstration des crimes. Une étude comparée du récit médiatique des violences extrêmes contre les civils durant la Grande Guerre

2017· article· fr· W2593539582 on OpenAlexaffvenueabout
Joceline Chabot, Sylvia Kasparian

Bibliographic record

VenueRevue de l’Université de Moncton · 2017
Typearticle
Languagefr
FieldSocial Sciences
TopicMiddle East and Rwanda Conflicts
Canadian institutionsUniversité de Moncton
Fundersnot available
KeywordsHumanitiesArt

Abstract

fetched live from OpenAlex

Cet article présente les résultats d’une recherche comparée sur les récits médiatiques du « massacre des Arméniens » et des « atrocités allemandes » dans la presse canadienne francophone durant la Grande Guerre. Quelles sont les spécificités du récit de ces événements eu égard à la définition et la qualification des crimes perpétrés contre les populations civiles ? Le recours aux outils informatisés de données textuelles nous a permis de traiter 1 172 articles de presse afin de rendre visible et de comparer les termes par lesquels les « atrocités allemandes » commises contre les civils sur le front ouest en 1914 et le « massacre des Arméniens » de l’Empire ottoman en 1915-1916 se sont inscrits dans l’espace médiatique. Cette analyse comparative des actes impliquant des violences extrêmes contre les civils devrait valider notre hypothèse selon laquelle les contemporains ont compris le caractère systématique, intentionnel et radical des crimes commis par les autorités ottomanes contre la population arménienne.

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)0.008
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.117
Threshold uncertainty score0.232

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0030.004
Scholarly communication0.0030.002
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0120.001

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 head0.027
GPT teacher head0.257
Teacher spread0.230 · 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 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

Citations0
Published2017
Admission routes3
Has abstractyes

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Same venueRevue de l’Université de MonctonSame topicMiddle East and Rwanda ConflictsFrench-language works237,207