The Ideal Narratee and the Rhetorical Model of Audiences
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract This article proposes to revise rhetorical narrative theory's model of audiences in fiction (actual, authorial, narrative, ideal narrative, and narratee) by replacing the term/concept of the ideal narrative audience with that of the ideal narratee, defined as the audience the narrator wishes they were addressing. This revision calls attention to the various ways that authors can handle the relations between the actual narratee and the ideal narratee (the actual may—or may not—coincide with the ideal), and such variety, in turn, points to the need for a more general taxonomy of authorial uses of the narratee. This taxonomy identifies three recognizably distinct ranges along a single broad spectrum of degrees of alignment between actual and ideal narratees: (1) clear alignment, (2) uncertain alignment, and (3) non-alignment. The taxonomy also identifies two main variants within each category, based on how an author's handling of the degree of alignment guides their readers’ focus: is it primarily on the narrated, the narrating, or both? The essay demonstrates the interpretive payoffs of the taxonomy through its analysis of a wide array of case studies including Mohsin Hamid's Reluctant Fundamentalist, Andrew Marvell's “To His Coy Mistress,” Sandra Cisneros's “Barbie-Q,” and Albert Camus's Fall.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it