Bibliographic record
Abstract
In this chapter, we discuss the role of perceptual information in narrative and its role in identifying the narrator and his or her knowledge of the narrative world. In the traditional narratological scholarship, the role of perceptual information is only one small part of the broader problem of focalization. First, we illustrate several specific problems that have arisen in the theory of focalization as a result of the unresolved tension between a formal description of the text and a subjective description of what readers may do with that text. Second, we discuss some of the psychological evidence on perspective and spatial representations. Third, based on some of these ideas, we propose a psychonarratological solution to the problems we identify in the theory of focalization. This approach enables us to step out of the circular logic relating the text to ideal readers and vice versa. Fourth, we present some new ideas concerning the nature of the representations pertaining to focalization that readers construct. Finally, we describe some empirical evidence consistent with our framework and hypotheses. Narratological Approaches to Focalization Among scholars who have engaged in the narratological dialogue on focalization, there seems to be a relatively clear understanding about the theoretical goals: A theory of focalization should provide an account of the source of knowledge and perception within the text based on the relationship between the narrator and the characters.
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.015 |
| Scholarly communication | 0.006 | 0.013 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".