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Record W2969459219 · doi:10.7202/1060721ar

Retours sur l'affaire rwandaise

2004· article· fr· W2969459219 on OpenAlexvenueaboutno aff
Yves Tremblay

Bibliographic record

VenueBulletin d histoire politique · 2004
Typearticle
Languagefr
FieldSocial Sciences
TopicMiddle East and Rwanda Conflicts
Canadian institutionsnot available
Fundersnot available
KeywordsPolitical science

Abstract

fetched live from OpenAlex

p.Hatzfeld, Jean.Une saison de machettes.Récits, Paris, Éditions du Seuil, 2003, 318 p. (Prix Fémina de l'essai 2003 ).Philpot, Robin.Ça ne s'est pas passé comme ça à Kigali, Montréal, Les Édi, tians des Intouchables, 2003, 221 p. Voici trois autres livres sur le désastre rwandais, trois visions différentes mais toutes dérangeantes d'un drame qui aurait pu être évité.Commençons par les témoignages.D'une certaine manière, Hatzfeld et Dallaire examinent l'affaire rwandaise à travers le même prisme: quels sont les ressorts psychologiques du génocide?Vun scrute les mécanismes mentaux chez les génocidaires, l'autre chez l'acteur impuissant et repentant.Jean Hatzfeld donne la parole aux principaux témoins à charge, les tueurs.Cela pourra paraître outrageant dans nos sociétés de « victimisation» complaisante.Entreprise de justification?Rien à craindre, car Hatzfeld a fixé des règles de participation stricte à son groupe de témoins 1 • Les dix témoins étaient des prisonniers en attente de procès.À première vue, la plupart ont un air respectable 2 • Mais ces hommes sont des bourreaux d'une efficacité monstrueuse.Leurs armes de destruction massive, ce sont les préjugés, les fausses rumeurs racistes, la propagande de bouche à oreille ou radiophonique.La machette est un accessoire utile,-mais le couteau de cuisine, le ciseau du tailleur ou le bâton du berger aurait tout aussi bien fait Association québécoise d'histoire politique

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)0.001
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: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.064
Threshold uncertainty score0.128

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0070.003
Scholarly communication0.0040.003
Open science0.0000.003
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0190.004

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 head0.018
GPT teacher head0.246
Teacher spread0.228 · 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

Citations1
Published2004
Admission routes2
Has abstractyes

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