Bibliographic record
Abstract
The title of this chapter is perhaps presumptuous because it suggests that some problems have been solved. In fact, even for issues we dealt with in some depth, such as narratorial implicatures and narrator–character associations, the present work merely scratches the surface. Thus, it is perhaps more appropriate to regard this work as an outline of an approach or framework, and the presented research provides only an illustration of the kind of work that can be done within that framework. Further, although we have attempted to cover a broad class of issues in the processing of narrative, there are many areas on which we have not touched. In this chapter, we discuss how psychonarratology could be developed to deal with some of these. First, we recapitulate what we see as the essential ingredients in our approach and summarize some of the specific ideas we have applied to the classic issues in narratology and literary studies. Following that, we discuss some important complications that we have glossed over in our treatment of these issues. Then, we describe some of the other obvious areas in which our treatment has yet to be applied but for which it seems ideally suited. Finally, we mention a few allied domains for which psychonarratology may have implications. The Psychonarratology Approach Core Assumptions Psychonarratology is an interdisciplinary approach to the study of the processing of narrative form.
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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 | 0.020 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.006 | 0.025 |
| Scholarly communication | 0.009 | 0.035 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.012 | 0.017 |
| Insufficient payload (model declined to judge) | 0.036 | 0.007 |
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".