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Record W2888280835 · doi:10.1353/eir.2018.0005

Emma Donoghue: Voicing the Nobodies in the Biographical Novel

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

Bibliographic record

VenueÉire-Ireland · 2018
Typearticle
Languageen
FieldArts and Humanities
TopicShort Stories in Global Literature
Canadian institutionsnot available
Fundersnot available
KeywordsMistakeOrder (exchange)Resistance (ecology)LiteratureHistoryVoiceArt historyArtClassicsPhilosophyLawLinguistics

Abstract

fetched live from 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...

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 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 score0.935
Threshold uncertainty score0.561

Codex and Gemma teacher scores by category

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

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 head0.023
GPT teacher head0.242
Teacher spread0.219 · 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 teacher head, 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

Citations4
Published2018
Admission routes1
Has abstractyes

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