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Record W2013121888 · doi:10.4000/narratologie.6779

Entrecroisements d’humour et modernité dans la seconde moitié du XIXe siècle : le cas Dossi

2013· article· fr· W2013121888 on OpenAlexaff
Antonio Saccone

Bibliographic record

VenueCahiers de narratologie · 2013
Typearticle
Languagefr
FieldArts and Humanities
TopicHistorical and Literary Analyses
Canadian institutionsAgriculture and Agri-Food Canada
Fundersnot available
KeywordsHumanitiesArt

Abstract

fetched live from OpenAlex

Dans les Note azzurre [Notes bleues] de Carlo Dossi se manifeste constamment l’intérêt de l’auteur pour l’humour. Cet intérêt se manifeste surtout dans le matériel rassemblé en vue du projet d’une Storia dell’umorismo [Histoire de l’humour] et d’autres œuvres similaires, comme les Note umoristiche di letteratura alta e bassa [Notes humoristiques de littérature haute et basse] ou Il libro delle bizzarrie [Le livre des bizarreries]. Dossi considère l’humour comme la seule perspective de connaissance (et seule ressource expressive) praticable dans la trame de la modernité. Parmi les ascendants européens de l’humour de Dossi, une place de choix est occupée par les « excentriques » allemands et anglais du XVIIIe siècle, auteurs de récits constellés de digressions paradoxales, de détails hypertrophiques, en premier lieu Jean Paul et Laurence Sterne. Deux autres grands modèles animent le discours théorique et narratif de Dossi : Alessandro Manzoni et Giuseppe Rovani. Or, Dossi met Manzoni et Rovani au service de ses idées et de sa pratique d’écriture : c’est pourquoi il se place lui-même, après ses deux auctores, comme point culminant de la parabole dessinée par l’hypothétique Storia dell’umorismo.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.605
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.002
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.226
Teacher spread0.211 · 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; both teacher heads agree on what is shown here.

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

Citations0
Published2013
Admission routes1
Has abstractyes

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