Bibliographic record
Abstract
In the present chapter, we present a framework and methodology for the empirical study of psychonarratology and discuss some of the epistemological issues that form the background for this kind of research. Following a discussion of the domain of psychonarratology, we elaborate on four aspects of the methodology that are central to its study. First, we discuss the distinction between features and constructions introduced in Chapter 1 and describe criteria for developing useful textual features. Second, the term “statistical reader” is introduced; this term describes an approach in which aggregate measures of groups of individuals are used to provide insights into the general characteristics of populations of readers. Third, we sketch some of the epistemological assumptions involved in conducting empirical research in psychonarratology and outline the theoretical goals. Fourth, we argue that the strongest inferences about reading processes can only be obtained by conducting “textual experiments” in which the text is manipulated and concomitant changes in readers' responses are observed. Together, these notions provide a foundation for the empirical investigation of the problems of psychonarratology. This chapter was designed in part to address the needs, interests, and concerns of literary scholars who may be intrigued by the empirical study of literary response but lack the confidence to pursue it on their own. In particular, we have endeavored to outline the fundamentals of empirical research without recourse to specialized knowledge or vocabulary. As we discuss in Chapter 1, the very word “statistics” may have unpleasant connotations for literary scholars.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.167 | 0.090 |
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".