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Record W1918626459 · doi:10.4000/lrf.239

« Sur fond de cruelle inhumanité » ; les politiques du massacre dans la Révolution de Haïti.

2022· article· fr· W1918626459 on OpenAlexaff
Bernard Gainot

Bibliographic record

VenueLa Révolution française · 2022
Typearticle
Languagefr
FieldArts and Humanities
TopicHistorical Studies and Socio-cultural Analysis
Canadian institutionsInstitut d'Histoire de l'Amérique Française
Fundersnot available
KeywordsHumanitiesArtEthnologyPhilosophySociology

Abstract

fetched live from OpenAlex

On peut caractériser fort justement la situation coloniale comme une « société de violence ». De la violence ordinaire au massacre racial, il y a toutefois des gradations, un changement d’échelle, dont il s’agit de mesurer la nature, la positivité, et la portée. Pour ce faire, nous prendrons la période qui voit l’émergence de l’Etat indépendant de Haïti, entre 1799 et 1804. Nous concentrerons notre attention sur les massacres raciaux commis par le corps expéditionnaire français, que certains ouvrages récents ont placé au centre de polémique par l’utilisation d’expressions telles que « génocide » ou « crimes contre l’humanité ». Nous chercherons donc successivement à isoler le massacre par rapport à l’héritage colonial ; à évaluer la positivité du fait lui-même (la société coloniale est un univers de la peur, propice à la propagation de la rumeur) ; enfin à recontextualiser celui-ci, non pour en relativiser la portée, mais pour souligner combien une « politique du massacre », en une symbolique qui mêle le sang et la race, a pu être constitutive de la formation de la nation haïtienne.

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.161
Threshold uncertainty score0.320

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0080.012
Scholarly communication0.0030.002
Open science0.0000.002
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0050.001

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 head0.015
GPT teacher head0.211
Teacher spread0.196 · 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
Published2022
Admission routes1
Has abstractyes

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