Self-Representation in Fiction: The Use of Author Characters from Inclusion to Puppetry
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.016 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".