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Record W4226139624 · doi:10.4000/conflits.22798

Imaginer la possibilité de la guerre nucléaire pour y faire face

2021· article· fr· W4226139624 on OpenAlexaff
Benoît Pélopidas

Bibliographic record

VenueCultures & conflits/Cultures et conflits · 2021
Typearticle
Languagefr
FieldSocial Sciences
TopicGeographies of human-animal interactions
Canadian institutionsCanadian Institute for International Peace and Security
Fundersnot available
KeywordsHumanitiesArtPolitical science

Abstract

fetched live from OpenAlex

Comment les citoyens peuvent-ils se figurer la possibilité de la guerre nucléaire pour y faire face politiquement ? Pour répondre à cette nouvelle question, cette intervention s’inscrit dans la lignée des travaux sur la culture populaire visuelle et avance trois arguments. D’abord, elle réaffirme le rôle essentiel de l’imagination et de la forme fictionnelle. Ensuite, elle identifie quatre gestes esthétiques qui rendent la possibilité de la guerre nucléaire imaginable. Enfin, à partir d’une étude de la culture populaire visuelle portant sur la catastrophe nucléaire de 1951 à 2019, elle requalifie la période post-1990 comme moment de production d’un imaginaire dans lequel la guerre nucléaire est absente et les armes potentiellement salutaires, par contraste avec les quatre décennies précédentes. Elle s’appuie sur des matériaux empiriques inédits : une campagne d’entretiens auprès de ceux qui perpétuent, luttent contre, filment ou éduquent au sujet de la guerre nucléaire, en France, au Royaume-Uni et aux États-Unis ainsi qu’un sondage de grande ampleur de la population des États européens dotés d’armes nucléaires ou hébergeant des armes nucléaires américaines.

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)0.003
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.023
Threshold uncertainty score0.063

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0110.020
Scholarly communication0.0090.007
Open science0.0010.005
Research integrity0.0050.004
Insufficient payload (model declined to judge)0.0190.003

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 head0.025
GPT teacher head0.369
Teacher spread0.344 · 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 designTheoretical or conceptual
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

Citations6
Published2021
Admission routes1
Has abstractyes

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