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Record W2063232344 · doi:10.7202/019281ar

La chasse au monstre

2008· article· fr· W2063232344 on OpenAlexvenueno aff
Marie-Hélène Larochelle

Bibliographic record

VenueÉtudes littéraires · 2008
Typearticle
Languagefr
FieldArts and Humanities
TopicLinguistics and Discourse Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsInvectiveHumanitiesArtPolitical scienceLaw

Abstract

fetched live from OpenAlex

Les écrits anarchistes d’Émile Pouget montrent de façon caricaturale le prolétaire s’attaquant à l’État. Ce renversement des pouvoirs métaphorise un tableau de chasse à courre caricatural selon lequel la bête chasse la noblesse. Le présent article entend explorer cette allégorie afin de comprendre la nature et la portée du vecteur central de l’invective. Est privilégié le corpus restreint des écrits du père Peinard, personnage inventé par Pouget, soit la revue du Père Peinard et Les almanachs du Père Peinard. Pour cerner la force de l’invective littéraire et mettre en avant la croisée — entre la fiction et la réalité —, cet article fait intervenir une nouvelle notion, l’invectif, par opposition au performatif, qui entend cerner la spécificité de l’invective en contexte fictionnel.

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)0.003
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 score0.017
Threshold uncertainty score0.045

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0060.008
Scholarly communication0.0040.003
Open science0.0010.002
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0140.002

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 head0.041
GPT teacher head0.265
Teacher spread0.224 · 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
Published2008
Admission routes1
Has abstractyes

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