Bibliographic record
Abstract
Abstract This article is concerned with the moment-by-moment unfolding of the text as we might suppose the reader to experience it; in addressing one aspect of this reading experience, I propose a definition of the episode, and of episode structure, in literary narratives. To do so, I draw on insights from Ingarden, Iser, Barthes, Eco, Jim Rosenberg, and Ed Tan, but have found most useful the discussion of narrative structure in a 1922 essay by the Russian Formalist A. A. Reformatsky, which includes an analysis of Maupassant's story “Un Coq Chanta”. Reformatsky's essay is analyzed in detail. In a final section, I review responses to a short story (Kate Chopin's “The Story of an Hour”) and consider the evidence for episodes in readers' responses. To the number of convergent criteria used for characterizing episodes I add the role of the narrative twist occurring at or near the end of an episode, serving to intensify or redirect the issues raised, and itself characterized by a distinct development in readers' feeling. Episodes provide the phases during which issues of concern to readers are managed and developed, and the analysis of the episodes of a story may thus provide a valuable framework for identifying the key developments in the responses of readers.
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 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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".