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Record W2973450498 · doi:10.1353/vcr.2019.0015

Seeming Human: Artificial Intelligence and Victorian Realist Character by Megan Ward

2019· article· en· W2973450498 on OpenAlexvenueno aff
Tara K. MacDonald

Bibliographic record

VenueVictorian review · 2019
Typearticle
Languageen
FieldArts and Humanities
TopicHistory of Science and Medicine
Canadian institutionsnot available
Fundersnot available
KeywordsCharacter (mathematics)RealismVerisimilitudeEpistemologyAestheticsSociologySituatedPosthumanismHumanismReflexivityLiteraturePhilosophyArtComputer scienceArtificial intelligenceSocial science

Abstract

fetched live from OpenAlex

Reviewed by: Seeming Human: Artificial Intelligence and Victorian Realist Character by Megan Ward Tara MacDonald (bio) Seeming Human: Artificial Intelligence and Victorian Realist Character by Megan Ward; pp. 216. Ohio State UP, 2018. $84.81 cloth. What does the work of Anthony Trollope and Alan Turing have in common? More than you might think, according to Megan Ward’s innovative new book, Seeming Human. It is an original and playful account of the ways in which we can see mid-twentieth-century forms of artificial intelligence as both “afterlives of and theories for realist character” (99). Ward emphasizes that critics have read the history of the realist novel largely through its use of formal techniques of interiority, privileging free indirect discourse and interior monologue. She argues that while we are comfortable with acknowledging realism’s self-reflexivity, we are less comfortable admitting the fictionality, or the “non-human-ness,” of fictional characters (2). Since characters are words on a page that are meant to seem human, Ward argues [End Page 287] that intelligent machines are more useful than actual humans in offering us a theory of character verisimilitude. Such claims allow her to attend to aspects of realist character that have been considered anti-realist or awkward, such as mechanicity and flatness. She suggests that we have situated these traits outside of realism not because they are inherently anti-realist but, rather, because we do not want to identify such qualities as human. Rather than sticking to the humanist tradition, we might imagine realist character as part of the history of the posthuman. Each chapter pairs an aspect of fictional character—development, predictability, flatness, and mind—with forms of AI. Chapter 1 uses the cybernetic feedback loop to explore repetitive middle-class domesticity in novels by Charlotte Mary Yonge, Elizabeth Gaskell, and Margaret Oliphant. Chapter 2 pairs information theory, specifically the stochastic system, with patterns of predictability in Dickens’s Bleak House, Collins’s No Name, and Braddon’s Lady Audley’s Secret. Turing’s imitation game, better known as the Turing Test, is used to read Trollope’s so-called flat characters Lizzie Eustace and Ferdinand Lopez in chapter 3. And, finally, chapter 4 contrasts two models of artificial mind—the perceptron and the physical symbol system—with Thomas Hardy’s and Henry James’s differing representations of human consciousness. The brief coda, on David Lodge’s novel Thinks . . . (2001), serves as a somewhat odd conclusion to the book. Seeming Human offers various new ways to conceive of character and specifically focuses on how we can think about character representation outside of narrative techniques and even cultural values that stress psychological interiority. In chapter 1, the feedback loop, which “need not result in transformation to show adaptation,” is used to show that development, for characters, need not be synonymous with change (23). Instead, development can happen through automated daily routine. In Yonge’s The Daisy Chain (1856), she argues, characters’ reliance on domestic routine—a project of edifying self-making—does not make them any less realist. Ward’s notion that embodied acts of routine do not always gesture to or symbolize a hidden self, but instead, are the self is the first of her attempts to separate character from interiority. Indeed, this is key to the second chapter, which focuses on mystery and sensation fiction. She convincingly points out that characters in sensation novels have been dismissed as superficial or repetitive, but that if we reorient our attention from psychological depth to an understanding of the human as part of a system, then these novels have much to tell us about the formation of character. She builds on, and challenges, Alex Woloch’s work on character systems, resisting the idea that secondary or flat characters only help to differentiate the protagonist in novels like Bleak House; instead, characters like Skimpole also work to “accrete meaning across the surface of the novel” (51). Thinking about character as information circulation, the chapter offers clever close readings of Lady Dedlock’s portrait, of blank spaces in No Name, and of the multiple versions of Lady Audley. [End Page 288] Building on these claims, chapter 3 presents “a theory of realist...

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.911
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.001

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 head0.050
GPT teacher head0.271
Teacher spread0.221 · 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.

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".

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Citations0
Published2019
Admission routes1
Has abstractyes

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