Understanding Difficulty: Reader Response and Cognition Across Genres
Bibliographic record
Abstract
Reader response and reader reception theories of the twentieth-century have left one area of research curiously unexamined--readers. From the Russian Formalists (Victor Shklovksy), to the Constance school (Hans Robert Jauss and Wolfgang Iser), to the poststructuralists (Roland Barthes, Jacques Derrida), the focus of theorists has always been on texts and the characteristics that define their literariness. Readers--in actuality, a multitude of real individuals with various aptitudes, experiences and tastes--have been abstracted into "the reader," a hypothetical everyman. As such, a vast section on the spectrum of possible reader responses, which may include interest, intrigue and enjoyment, but also frustration, boredom and annoyance, has been regretfully ignored in the discipline of literary studies. Texts that present readers with difficulties--either linguistic or logical--highlight especially well the wide array of possible reader responses, for experimental art (whether visual or written) sets out to defy expectations, aiming precisely to incite controversy and divide opinions. This dissertation therefore takes up works published in the late 1960s and early 1970s that are known for their difficult, experimental styles and studies how readers respond to them, taking particular interest in the cognitive processes that are involved in the act of reading. Under the lens are Thomas Pynchon's
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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 | 0.006 | 0.086 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.013 | 0.014 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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.
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".