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Enregistrement W2888280835 · doi:10.1353/eir.2018.0005

Emma Donoghue: Voicing the Nobodies in the Biographical Novel

2018· article· en· W2888280835 sur OpenAlexaboutno aff
Michael Lackey, Emma Donoghue

Notice bibliographique

RevueÉire-Ireland · 2018
Typearticle
Langueen
DomaineArts and Humanities
ThématiqueShort Stories in Global Literature
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésMistakeOrder (exchange)Resistance (ecology)LiteratureHistoryVoiceArt historyArtClassicsPhilosophyLawLinguistics

Résumé

récupéré en direct d'OpenAlex

Emma Donoghue: Voicing the Nobodies in the Biographical Novel Michael Lackey (bio) and Emma Donoghue Born in dublin in 1969, Emma Donoghue earned a Ph.D. in eighteenth-century literature at Cambridge University before moving to Canada. She is best known for her novel Room, which is set in the contemporary period. But much of her fiction is historical and fact-based, with settings ranging from the fourteenth to the twentieth centuries. Life Mask (2004), The Sealed Letter (2008), and Frog Music (2014) in particular are biographical novels closely based on real historical figures. michael lackey: Past writers frequently based their novels on actual historical figures, but authors changed their subjects’ names in order to allow themselves more creative freedom. The biographical novel is different because it names its protagonist after a specific figure—a huge mistake according to Georg Lukács.1 emma donoghue: I think those of us who have written about real figures have always run into people over the years who say, “Big mistake, you shouldn’t use the actual name.” In fact, they often say that it is a big mistake to write anything historical because some people think writing about anything but the present day is a failure to live up to your responsibility to speak for your moment. ml: Given this resistance, why do you think that the biographical novel came into being? And what are the benefits and drawbacks of doing a biographical novel? [End Page 120] ed: I think the reason I use a specific, real, named protagonist is because my original impulse was very much to represent the ones who had been left out—like the nobodies, women, slaves, people in freak shows, servants—the ones who are not powerful. I felt an obligation. If I was going to write about them at all, I wanted to give them their little moment in the sun. I wanted to name them, even if they were incredibly obscure figures. When I have written short stories, for instance, they have been about people so obscure that we only know maybe two things, a name and a fact. But still I try to get the details I know about the person right. But if you are writing about Henry VIII, he does not need any more fame, so you can make him into King Ludwig if you prefer. But if I am writing about this girl who was executed in the 1760s, and if I know that her name was Mary Saunders, then that is the name I should stick to. So it was a feeling of loyalty or wanting to represent them, not just to represent categories or classes, but the actual individuals. And in terms of the pros and cons of this literary choice, I find that with a historical figure readers absolutely love to know about the tiny little bit that is real. It is not as if they want the whole thing to be factual. They want an enjoyable fiction, but they just love that tiny little hook that holds onto the real. But I suppose the bigger question is why I choose real individuals at all. I often wonder why I am doing this complicated double job of all the historical research into the real guy and where he was living in 1820. And then the making of fiction as well, because of course researching the facts does not actually save me any work with the fiction. I still have to make up so much because the life below—the inner life—must be a mystery. So it is a double job, it is a complicated one, and all I can say is that the double job appeals to me or attracts me. I like how different those two obligations are: the obligation to find out what really happened and then the obligation to make it all up. I find that exciting. ml: Let me press you on this notion of historical accuracy. You express an interpretation about what prompted the killer to gun down Jenny Bonnet in Frog Music. Let us say, hypothetically, that somebody finds a journal, and we know for certain that the one you...

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: aucune
Score de désaccord entre enseignants0,935
Score d'incertitude au seuil0,561

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0010,001
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,023
Tête enseignante GPT0,242
Écart entre enseignants0,219 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations4
Publié2018
Routes d'admission1
Résumé présentoui

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