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Record W7115176367 · doi:10.4000/15ch3

Le roman noir est-il encore politique ? Figures de l’extrême-droite dans le polar contemporain

2025· article· pt· W7115176367 on OpenAlexvenueno aff

Bibliographic record

VenueBelphégor · 2025
Typearticle
Languagept
FieldArts and Humanities
TopicLiterature and Culture Studies
Canadian institutionsnot available
Fundersnot available
KeywordsStyle (visual arts)NarrativePoetryPortraitPeriod (music)

Abstract

fetched live from OpenAlex

Si les auteurs de polars contemporains se démarquent de l’héritage du néo-polar français engagé à gauche, certains poursuivent l’investigation sociale permise par le roman noir. Jouant sur la frontière entre la fiction policière et la non-fiction documentaire, les romans Le Bloc de Jérôme Leroy (2013), Aux animaux la guerre de Nicolas Mathieu (2014) et Ce qu’il faut de nuit de Laurent Petitmangin (2020) mettent en scène des situations qui convoquent le repli identitaire, des formations politiques fascisantes ou encore un racisme structurel, sans nécessairement prendre le parti de la dénonciation des discours extrémistes. Faut-il pour autant conclure à un « nihilisme » du roman noir français contemporain (Ledien, 2020) ou au renouveau d’un « style réactionnaire » (Berthelier, 2022) ? Ou bien peut-on y voir en filigrane de nouvelles écritures d’un engagement désabusé, qui lutterait en sourdine contre l’extrême droite ? Au-delà d’une analyse des postures sociolittéraires, et malgré leurs réticences face à un militantisme exacerbé, l’analyse littéraire démontre que ces auteurs portent de fait un discours critique acéré via le portrait en miroir d’une société française en voie de fascisation et grâce aux outils d’un réalisme renouvelé, sous le signe de la narration polyphonique et de la caricature sociologique.

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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.083
Threshold uncertainty score0.164

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 studies0.0080.017
Scholarly communication0.0060.003
Open science0.0000.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0080.001

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 head0.012
GPT teacher head0.239
Teacher spread0.226 · 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
Published2025
Admission routes1
Has abstractyes

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