On endotextuality: literary dystopias, texts-within-texts, and post-truth
Bibliographic record
Abstract
This thesis reconceptualises literary works’ additional fictional texts that have long been maligned, dismissed, and overlooked (“endotexts”), devises the theoretical framework that accounts for how they broadly work (“endotextuality”), and explains the specific functions of their different types (“endotextures”). It defines “endotexts” as additional texts that assume the guise of a written document and are only found printed within, alongside, or around a fictional work’s larger main narrative, from whose story-world they originate, and whose thematic concerns and central conceits they metafictionally crystallise, subsequently shaping real-world readers’ interpretations of those narratives and alerting them to the extratextual presence of comparable issues, thus challenging the assumptions of the world that they hold. This definition also addresses “endotextuality”, which brings together transtextuality, the tension between theories of reader-response and the mise en abyme, metafiction, and Barthesian bliss to explicate endotexts’ general aims. Additionally, it establishes three distinct “endotextures”: Written and Read Endotexts, Parallel Endotexts, and Critical Endotexts. This conceptual work is made in relation to literary dystopias and the phenomenon of post-truth. Twenty-two literary dystopias spanning a one-hundred-and-twelve-year period whose authors represent eleven different nations (Canada, China, the Czech Republic, Egypt, Finland, Germany, Japan, Poland, Russia, the United Kingdom, and the United States of America) have their endotexts analysed in this thesis. This long historical frame of reference and broad geographical span evinces that there is a longstanding tradition of endotextuality within the dystopian genre, whereas the selection of classical, canonical, and contemporary works demonstrates the potential for endotextual readings to uncover new meaning. By conducting these endotextual readings in reference to ideas relating to post-truth, this thesis argues that the dystopian genre possesses a transhistorical, transnational preoccupation with the problem of truth, which subsequently challenges the scholarly assumption that literary dystopias relate only to their specific geohistorical contexts. This study considers five groups or institutions that literary dystopias – particularly through their endotexts – warn may be responsible for disregarding, distorting, and abusing truth: political leaders, historical experts, the Fourth Estate, digital and social medias, and laypeople. These first four groups represent different arbiters of truth, whereas the final entity represents those arbiters’ typical audience, and so this thesis examines how truth claims are made, by who, and how they are received. Such considerations are therefore peculiarly available to the intensified post-truth condition that has come to characterise the twenty-first century. It sees this thesis propose that dystopian endotexts are uniquely positioned to help readers to resist the pervasiveness and persuasiveness of post-truth, for they challenge the assumptions readers may hold of the world, precisely scrutinise the strategies used to stoke post-truth’s flames, and demand that readers exercise the same degree of active criticality when posed with extratextual truth claims as they do when undertaking the complex interpretive processes engendered by endotexts. As such, this thesis highlights the urgency of dystopian endotexts and the necessity of their new reconceptualization.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.008 | 0.045 |
| Scholarly communication | 0.008 | 0.013 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| 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".