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Record W7132895401

Self-Representation in Fiction: The Use of Author Characters from Inclusion to Puppetry

2024· dissertation· W7132895401 on OpenAlexafffund
Alexander Samuel Book Sarra-Davis

Bibliographic record

VenueTSpace · 2024
Typedissertation
Language
FieldEconomics, Econometrics and Finance
TopicNew Zealand Economic and Social Studies
Canadian institutionsUniversity of Toronto
FundersUniversity of Toronto
KeywordsConverseScrutinyLiterary criticismInclusion (mineral)NarrativeReinterpretationCopyingTransition (genetics)White (mutation)
DOInot available

Abstract

fetched live from OpenAlex

This dissertation identifies a previously unarticulated literary device, authorial self-representation in fiction, and examines three authors who, in the last two decades, have published novels with representations of themselves as characters. While authors have always appeared as narrators in the genre of life-writing, their appearance within fiction has until recently been a much rarer phenomenon. Like readers, authors are not fictional creations, and so are not expected to exist within their novels; yet, by rendering themselves as a named character within their fiction, authors violate literary convention and create opportunities for them to converse with and be acted upon by their fictional subjects. Although the figure of the author has been much theorized in the last half-century, such scrutiny has focused on their role in literary production and reception, rather than the narratological and agential consequences of their appearance as characters within fiction. Explicitly addressing that gap in scholarship, this dissertation examines texts from three authors—white Afrikaner Australian J. M. Coetzee, first-generation Chicano immigrant Salvador Plascencia, and half-Japanese half-“Caucasian-American” Ruth Ozeki—who have deployed self-representation in their recent novels and proposes a theory of how and to what end this solidifying literary device is being used, especially in works that are post-colonial in nature. I argue that it is no coincidence self-representation is being pioneered by authors who occupy hybrid positionalities in the identity-obsessed literatures of white settler states, and that self-representation offers these authors a post-colonial strategy for proleptically addressing identitarian pressures. This dissertation further argues that self-representation allows for the apparent expansion of a novel’s scope and stakes, either working to reinforce the novel’s fictional events and characters through what I call self-inclusion or to cheapen them through what I call self-puppetry. By drawing from theories adjacent to these concerns, ranging from chronological suspense to psychological interiority and from authorial confession to the act of reading, and by applying them to novels like Diary of a Bad Year (2007), The People of Paper (2005), and A Tale for the Time Being (2013), this dissertation establishes a theoretical foundation and methodology for studying this growing literary trend.

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)0.009
Version: metacan-v3-hybrid-931329e0061cValidation 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: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0090.016
Scholarly communication0.0070.004
Open science0.0010.005
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.076
GPT teacher head0.320
Teacher spread0.244 · 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 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
Published2024
Admission routes2
Has abstractyes

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